sentiment_analysis_covid19_tweets.ipynb

{
  "cells": [
    {
      "cell_type": "markdown",
      "source": [
        "# Authors\n",
        "* Georgios Nikolaou    - 1115202000154 </br>\n",
        "* Styliani Fragkiadaki - 1115201900205"
      ],
      "metadata": {
        "id": "kB2Sv9VeQRgf"
      }
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "ozjL-04GMo2B"
      },
      "source": [
        "# Importing essential libraries"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "bTEawW_H1E8S",
        "outputId": "89c57b59-f120-48fc-c476-97df23d6409f"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "[nltk_data] Downloading package stopwords to /root/nltk_data...\n",
            "[nltk_data]   Unzipping corpora/stopwords.zip.\n",
            "[nltk_data] Downloading package punkt to /root/nltk_data...\n",
            "[nltk_data]   Unzipping tokenizers/punkt.zip.\n",
            "[nltk_data] Downloading package wordnet to /root/nltk_data...\n",
            "[nltk_data]   Unzipping corpora/wordnet.zip.\n",
            "[nltk_data] Downloading package omw-1.4 to /root/nltk_data...\n",
            "[nltk_data]   Unzipping corpora/omw-1.4.zip.\n",
            "Collecting pyLDAvis\n",
            "  Downloading pyLDAvis-3.3.1.tar.gz (1.7 MB)\n",
            "\u001b[K     |████████████████████████████████| 1.7 MB 5.2 MB/s \n",
            "\u001b[?25h  Installing build dependencies ... \u001b[?25l\u001b[?25hdone\n",
            "  Getting requirements to build wheel ... \u001b[?25l\u001b[?25hdone\n",
            "  Installing backend dependencies ... \u001b[?25l\u001b[?25hdone\n",
            "    Preparing wheel metadata ... \u001b[?25l\u001b[?25hdone\n",
            "Requirement already satisfied: scipy in /usr/local/lib/python3.7/dist-packages (from pyLDAvis) (1.4.1)\n",
            "Requirement already satisfied: future in /usr/local/lib/python3.7/dist-packages (from pyLDAvis) (0.16.0)\n",
            "Requirement already satisfied: jinja2 in /usr/local/lib/python3.7/dist-packages (from pyLDAvis) (2.11.3)\n",
            "Collecting funcy\n",
            "  Downloading funcy-1.17-py2.py3-none-any.whl (33 kB)\n",
            "Requirement already satisfied: joblib in /usr/local/lib/python3.7/dist-packages (from pyLDAvis) (1.1.0)\n",
            "Requirement already satisfied: setuptools in /usr/local/lib/python3.7/dist-packages (from pyLDAvis) (57.4.0)\n",
            "Requirement already satisfied: gensim in /usr/local/lib/python3.7/dist-packages (from pyLDAvis) (3.6.0)\n",
            "Requirement already satisfied: pandas>=1.2.0 in /usr/local/lib/python3.7/dist-packages (from pyLDAvis) (1.3.5)\n",
            "Requirement already satisfied: sklearn in /usr/local/lib/python3.7/dist-packages (from pyLDAvis) (0.0)\n",
            "Requirement already satisfied: numpy>=1.20.0 in /usr/local/lib/python3.7/dist-packages (from pyLDAvis) (1.21.6)\n",
            "Requirement already satisfied: scikit-learn in /usr/local/lib/python3.7/dist-packages (from pyLDAvis) (1.0.2)\n",
            "Requirement already satisfied: numexpr in /usr/local/lib/python3.7/dist-packages (from pyLDAvis) (2.8.1)\n",
            "Requirement already satisfied: pytz>=2017.3 in /usr/local/lib/python3.7/dist-packages (from pandas>=1.2.0->pyLDAvis) (2022.1)\n",
            "Requirement already satisfied: python-dateutil>=2.7.3 in /usr/local/lib/python3.7/dist-packages (from pandas>=1.2.0->pyLDAvis) (2.8.2)\n",
            "Requirement already satisfied: six>=1.5 in /usr/local/lib/python3.7/dist-packages (from python-dateutil>=2.7.3->pandas>=1.2.0->pyLDAvis) (1.15.0)\n",
            "Requirement already satisfied: smart-open>=1.2.1 in /usr/local/lib/python3.7/dist-packages (from gensim->pyLDAvis) (6.0.0)\n",
            "Requirement already satisfied: MarkupSafe>=0.23 in /usr/local/lib/python3.7/dist-packages (from jinja2->pyLDAvis) (2.0.1)\n",
            "Requirement already satisfied: packaging in /usr/local/lib/python3.7/dist-packages (from numexpr->pyLDAvis) (21.3)\n",
            "Requirement already satisfied: pyparsing!=3.0.5,>=2.0.2 in /usr/local/lib/python3.7/dist-packages (from packaging->numexpr->pyLDAvis) (3.0.8)\n",
            "Requirement already satisfied: threadpoolctl>=2.0.0 in /usr/local/lib/python3.7/dist-packages (from scikit-learn->pyLDAvis) (3.1.0)\n",
            "Building wheels for collected packages: pyLDAvis\n",
            "  Building wheel for pyLDAvis (PEP 517) ... \u001b[?25l\u001b[?25hdone\n",
            "  Created wheel for pyLDAvis: filename=pyLDAvis-3.3.1-py2.py3-none-any.whl size=136898 sha256=ea5c464878dff1959815c2e6f5302f0bb1bec28818aa708274841563be660064\n",
            "  Stored in directory: /root/.cache/pip/wheels/c9/21/f6/17bcf2667e8a68532ba2fbf6d5c72fdf4c7f7d9abfa4852d2f\n",
            "Successfully built pyLDAvis\n",
            "Installing collected packages: funcy, pyLDAvis\n",
            "Successfully installed funcy-1.17 pyLDAvis-3.3.1\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "/usr/local/lib/python3.7/dist-packages/past/types/oldstr.py:5: DeprecationWarning: Using or importing the ABCs from 'collections' instead of from 'collections.abc' is deprecated since Python 3.3,and in 3.9 it will stop working\n",
            "  from collections import Iterable\n"
          ]
        }
      ],
      "source": [
        "import pickle\n",
        "import numpy as np\n",
        "import pandas as pd\n",
        "pd.set_option('display.max_colwidth', 700) \n",
        "import re\n",
        "import matplotlib.pyplot as plt\n",
        "from wordcloud import WordCloud\n",
        "import time\n",
        "from collections import Counter\n",
        "\n",
        "\n",
        "import nltk\n",
        "from nltk.corpus import stopwords\n",
        "from nltk.tokenize import word_tokenize\n",
        "from nltk.stem import WordNetLemmatizer, PorterStemmer\n",
        "nltk.download('stopwords')\n",
        "nltk.download('punkt')\n",
        "nltk.download('wordnet')\n",
        "nltk.download('omw-1.4')\n",
        "\n",
        "\n",
        "from sklearn.model_selection import cross_validate, KFold, train_test_split\n",
        "from sklearn.metrics import make_scorer, accuracy_score, precision_score, recall_score, f1_score\n",
        "from sklearn.feature_extraction.text import CountVectorizer, TfidfVectorizer\n",
        "from sklearn import svm\n",
        "from sklearn.ensemble import RandomForestClassifier\n",
        "from sklearn.neighbors import KNeighborsClassifier\n",
        "\n",
        "\n",
        "from gensim.corpora.dictionary import Dictionary\n",
        "from gensim.models.ldamodel import LdaModel\n",
        "!pip3 install pyLDAvis\n",
        "import pyLDAvis\n",
        "from gensim.models import CoherenceModel\n",
        "import pyLDAvis.gensim_models as gensimvis\n",
        "\n",
        "import warnings\n",
        "warnings.filterwarnings(\"ignore\") \n",
        "\n",
        "from sklearn.model_selection import GridSearchCV, RandomizedSearchCV\n",
        "from sklearn.metrics import classification_report"
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "# Mount drive to store and have access to the files"
      ],
      "metadata": {
        "id": "46pSeynZnoKp"
      }
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "84xz-1wz627_",
        "outputId": "d5e01621-0040-4260-a79b-880cc220d98d"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Mounted at /content/gdrive\n"
          ]
        }
      ],
      "source": [
        "from google.colab import drive\n",
        "\n",
        "drive.mount('/content/gdrive')\n",
        "ROOT_PATH = 'gdrive/My Drive/tede_data/'  \n",
        "\n",
        "VECTORIZERS = ROOT_PATH + 'vectorizers/'\n",
        "DATASETS = ROOT_PATH + 'datasets/'\n",
        "GLOVE = ROOT_PATH + 'glove/'"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "mo66Rhcm2YUN"
      },
      "source": [
        "# Utility functions"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "U-8eAAvz2Xxr"
      },
      "outputs": [],
      "source": [
        "def write_data(path, obj):\n",
        "    with open(path, 'wb') as file:\n",
        "        pickle.dump(obj, file)\n",
        "\n",
        "def load_data(*paths):\n",
        "    assert(paths)\n",
        "    to_return = [pd.read_pickle(path) for path in paths]\n",
        "    return to_return if len(to_return) != 1 else to_return[0]\n",
        "\n",
        "def load_glove_model(path):\n",
        "    glove_model = {}\n",
        "    with open(path,'r') as f:\n",
        "        for line in f:\n",
        "            tokens = line.split()\n",
        "            glove_model[tokens[0]] = np.array(tokens[1:], dtype=np.float64)\n",
        "\n",
        "    return glove_model"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "96SzYKODYaBj"
      },
      "source": [
        "# Functions for plotting data"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "2naGKLSeYb8g"
      },
      "outputs": [],
      "source": [
        "def pie_plot(values, lables, title):\n",
        "    total = sum(values)\n",
        "    perc = [v / total for v in values]\n",
        "    plt.pie(perc, labels = lables)\n",
        "    plt.title(title)\n",
        "    plt.show() \n",
        "\n",
        "def hor_bar_plot(height, bars, title):\n",
        "    y_pos = np.arange(len(bars))\n",
        "    plt.barh(y_pos, height)\n",
        "    plt.yticks(y_pos, bars)\n",
        "    plt.title(title)\n",
        "    plt.show()\n",
        "\n",
        "def line_plot(x, y, title):\n",
        "    plt.plot( 'x_values', 'y_values', \n",
        "              data=pd.DataFrame({'x_values': x, 'y_values': y }), color='skyblue')\n",
        "    plt.title(title)\n",
        "    plt.show()"
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "# Question 1\n",
        "Lowercase the letters, remove mentions (@), hashtags (#) and links and then only keep the lower case english letters (removes emojis etc).\n",
        "This removes the numbers as well but they are not really important to understand the sentiment of the tweet.\n",
        "\n",
        "We finally remove the stopwords (the, and, ...) and we get the cleaned tweet"
      ],
      "metadata": {
        "id": "Xt1Gio7HgF1J"
      }
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "sY_fscZJ41zN"
      },
      "outputs": [],
      "source": [
        "def clean_tweets(tweets):\n",
        "    new_tweets = []\n",
        "    sw = stopwords.words('english') \n",
        "    sw.remove('not')\n",
        "    for tweet in tweets:\n",
        "        # Remove mentions, hashtags and links, on the now lowercase text\n",
        "        new_tweet = re.sub(r\"(@\\S+)|(#\\S+)|(http\\S+)|(www.\\S+)\",\"\", tweet.lower())\n",
        "\n",
        "        # Remove every character that is not a letter\n",
        "        new_tweet = re.sub(\"[^a-z]\",\" \", new_tweet)\n",
        "\n",
        "        # Remove stopwords\n",
        "        new_tweet = ' '.join([word for word in word_tokenize(new_tweet) if not word in sw])\n",
        "\n",
        "        new_tweets.append(new_tweet)\n",
        "\n",
        "    return new_tweets"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "DjuAwlM5lYKG"
      },
      "outputs": [],
      "source": [
        "df = load_data(DATASETS + 'data_set.pkl')\n",
        "df['text'] = clean_tweets(df['text'])\n",
        "write_data(DATASETS + 'data_set_clean.pkl', df)"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "3IGCP5TD5GaS"
      },
      "source": [
        "# Initial train-test split\n",
        "We split the data into 2 subsets, the training set that will be used to finetune the models and the test set that will be used to evaluate the final model that will have been fitted in all of the training set data."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "cFpFPNLy4_3h"
      },
      "outputs": [],
      "source": [
        "df = load_data(DATASETS + 'data_set_clean.pkl')\n",
        "\n",
        "train_data, test_data = train_test_split(df, test_size=0.2, random_state=1, stratify=df['sentiment'].values)\n",
        "write_data(DATASETS + 'train.pkl', train_data)\n",
        "write_data(DATASETS + 'test.pkl', test_data)"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "TJRayCiG5Jv9"
      },
      "source": [
        "# Question 2\n"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "tX3Z3wJI46Io"
      },
      "outputs": [],
      "source": [
        "def sentiments_distr(dataframe, title, feelings = ['NEG', 'NEU', 'POS']):\n",
        "    feelings_list = list(dataframe['sentiment'].values)\n",
        "    counts = [feelings_list.count(f) for f in feelings]\n",
        "\n",
        "    pie_plot(counts, tuple(feelings), title)\n",
        "\n",
        "def find_most_frequent(dataframe, title, most_frequent = 5, sentiment = None):\n",
        "    tweets = dataframe['text']\n",
        "    sentiments = dataframe['sentiment']\n",
        "    subset = tweets if sentiment == None else [t for (t,s) in zip(tweets, sentiments) if s == sentiment]\n",
        "\n",
        "    words = (' '.join(subset)).split()\n",
        "    c = Counter(words)\n",
        "    frequent = c.most_common(most_frequent)\n",
        "\n",
        "    hor_bar_plot([c[1] for c in frequent], tuple([c[0] for c in frequent]), title)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "ZIlEl58-lUvA"
      },
      "outputs": [],
      "source": [
        "df = load_data(DATASETS + 'train.pkl')"
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "## i. Sentiment distribution of tweets"
      ],
      "metadata": {
        "id": "mJqi8Gy5YLEe"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "sentiments_distr(df, \"Sentiment distribution of tweets\")"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 264
        },
        "id": "dBkQkbBgXobr",
        "outputId": "6d5eacd4-374a-4556-c6e0-25ab0d72d6a6"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ],
            "image/png": "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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "## ii. Most frequent words of tweets"
      ],
      "metadata": {
        "id": "fdYMgQbZYKrj"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "find_most_frequent(df, \"Most frequent words of tweets\")"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 281
        },
        "id": "rWNx_k1DXq_D",
        "outputId": "532b0292-8e77-4e43-d9b8-ae16e6685eb5"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ],
            "image/png": "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\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "## iii. Most frequent words of tweets depending on their sentiment"
      ],
      "metadata": {
        "id": "LzmZrU-cYKWB"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "find_most_frequent(df, \"Most frequent words of the negative tweets\", sentiment = 'NEG')\n",
        "find_most_frequent(df, \"Most frequent words of the neutral tweets\", sentiment = 'NEU')\n",
        "find_most_frequent(df, \"Most frequent words of the positive tweets\", sentiment = 'POS')"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 809
        },
        "id": "vEu1laHWXsFO",
        "outputId": "25e6d527-c46b-4b84-c53e-449a7e8115df"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ],
            "image/png": "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\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ],
            "image/png": "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\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ],
            "image/png": "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\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "## Sentiment distributions depending on vaccine"
      ],
      "metadata": {
        "id": "NOwBxPoLYKFR"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "astra_df = df[df['text'].str.contains('astrazeneca') == True]\n",
        "rest_vacc_df = df[df['text'].str.contains('moderna|pfizer|biontech') == True]\n",
        "\n",
        "sentiments_distr(astra_df, 'sentiments distribution for astrazeneca')\n",
        "sentiments_distr(rest_vacc_df, 'sentiment distribution for pfizer, moderna and biontech')"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 511
        },
        "id": "miYoTHpwXt6z",
        "outputId": "843b703d-d343-41ed-a110-987dfc398260"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ],
            "image/png": "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\n"
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ],
            "image/png": "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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "## v. Monthly tweet count"
      ],
      "metadata": {
        "id": "bN6t9Y1EXzyG"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "df['date'] = pd.to_datetime(df['date'])\n",
        "grouped = df.groupby([df['date'].dt.month]).agg('count')\n",
        "\n",
        "counts = grouped['id'].to_list()\n",
        "months = ('JAN','FEB','MAR','APR','MAY','JUN','JUL','AUG','SEP','OCT','NOV','DEC')\n",
        "\n",
        "hor_bar_plot(counts, months, 'tweets per month')"
      ],
      "metadata": {
        "id": "rRbPz0XPj7IV",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 281
        },
        "outputId": "72f7d011-152e-4a6d-8ca3-16082f54cc3c"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ],
            "image/png": "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\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "We observe that when big news about COVID-19 are announced, the number of tweets grows by a lot. We did some research on reliable sourses, and here are some interesting and noteworthy finds:\n",
        "\n",
        "June 2021 (1st in tweets) : Delta variant, a more deadly mutation of COVID-19 emerges in most countries of the world and it seems that even vaccined people could be in danger [https://www.nytimes.com/2021/06/22/health/delta-variant-covid.html] \n",
        "\n",
        "August 2021 (2nd in tweets) : Official FDA approval of the Pfizer vaccine. Pro-vaccine VS anti-vaccine conflict grows bigger and bigger [https://www.immunize.org/timeline/] \n",
        "\n",
        "March 2020 (4th in tweets) : WHO characterizes COVID-19 as a pandemic, and the first lockdowns are emerging worldwide. [https://www.yalemedicine.org/news/covid-timeline]"
      ],
      "metadata": {
        "id": "2SvABVINj8kD"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "## vi. Some observations of the data"
      ],
      "metadata": {
        "id": "kzno40lSX1H0"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "def sentiment_correlation(dataframe, title, column='retweets', sentiments = ['NEG', 'NEU', 'POS']):\n",
        "    sentiment_map = {'NEG' : 0, 'NEU' : 0, 'POS': 0}\n",
        "    sentiment_count = {'NEG' : 0, 'NEU' : 0, 'POS': 0}\n",
        "    for ind in dataframe.index:\n",
        "        sentiment_map[dataframe['sentiment'][ind]] += dataframe[column][ind]\n",
        "        sentiment_count[dataframe['sentiment'][ind]] += 1\n",
        "\n",
        "    # find averages\n",
        "    ratio = [sentiment_map[key] / sentiment_count[key] for key in sentiment_map.keys()]\n",
        "    print([(key, sentiment_map[key] / sentiment_count[key]) for key in sentiment_map.keys()])\n",
        "\n",
        "    hor_bar_plot(ratio, sentiments, title)"
      ],
      "metadata": {
        "id": "kZ6un9Jukhme"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "sentiment_correlation(df, 'retweet ratio among sentiments')"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 298
        },
        "id": "rWoYCWEtiCUh",
        "outputId": "266d2a15-14bc-4f65-e12c-e99489f855f4"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "[('NEG', 1.9179028652712864), ('NEU', 2.3711930972055946), ('POS', 3.7599179858859433)]\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ],
            "image/png": "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\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "The positive tweets get about 60% more retweets than neutral and 95% (!) more than negative tweets.\n",
        "\n",
        "We would expect that more controversial takes (positive or negative) would get the most retweets by quite a margin.\n",
        "\n",
        "But it appears that in these trying times, people did not want negativity, they needed to feel like things were getting better and this is reflected on their retweets."
      ],
      "metadata": {
        "id": "4pCoikGg0df_"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "sentiment_correlation(df, 'favorites ratio among sentiments', column='favorites')"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 298
        },
        "id": "d286NZPiiD57",
        "outputId": "a594bd9d-6805-40b2-dcd3-e3ef2b419755"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "[('NEG', 6.45954074375127), ('NEU', 10.142545332437878), ('POS', 20.792246805264163)]\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ],
            "image/png": "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\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "The above statement is supported by the ratio of favorites on the different tweets (meaning how much people liked the tweet), where positive tweets are favorited twice as much as the neutral tweets and three times as much as the negative tweets, which highlights the need people have had for positivity"
      ],
      "metadata": {
        "id": "tBl5j8FUiE0C"
      }
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "DKR0PKLq6Dvg"
      },
      "source": [
        "# Question 3"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "RiLmVam_46AQ"
      },
      "outputs": [],
      "source": [
        "MAX_DF = 1.0\n",
        "MIN_DF = 2\n",
        "MAX_FEATURES = 1000\n",
        "\n",
        "def prepare_emb(tweets, model_path):\n",
        "    model = pd.read_pickle(model_path)\n",
        "    vectorized = []\n",
        "    dims = len(list(model.values())[0])\n",
        "\n",
        "    for tweet in tweets:\n",
        "        vectors = [model[word] for word in tweet.split() if word in model]\n",
        "        vector = (sum(vectors) / len(vectors)) if vectors else np.zeros((dims,))\n",
        "        vectorized.append(vector)\n",
        "\n",
        "    return vectorized \n",
        "\n",
        "def prepare_vector(tweets, vectorizer_path):\n",
        "    vectorizer = load_data(vectorizer_path)\n",
        "    return vectorizer.transform(tweets)\n",
        "\n",
        "def fit_vectorizer(tweets, method, max_df=MAX_DF, min_df=MIN_DF, max_features=MAX_FEATURES):\n",
        "    vectorizer = method(max_df=max_df, min_df=min_df, max_features=max_features)\n",
        "    vectorizer.fit(tweets)\n",
        "    return vectorizer\n",
        "\n",
        "def transform_data(tweets, vec):\n",
        "    return vec.transform(tweets)\n",
        "\n",
        "def fit_transform_vectorizer(tweets, method, max_df=MAX_DF, min_df=MIN_DF, max_features=MAX_FEATURES):\n",
        "    vectorizer = method(max_df=max_df, min_df=min_df, max_features=max_features)\n",
        "    return vectorizer.fit_transform(tweets)"
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "Fit the vectorizers to the training set tweets and store them for future use\n",
        "\n",
        "For the word embeddings we use the pretrained glove model and therefore we don't have to do anything for that"
      ],
      "metadata": {
        "id": "w70-Sr-2p-pK"
      }
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "XUwEXKfulQGW"
      },
      "outputs": [],
      "source": [
        "df = load_data(DATASETS + 'train.pkl')\n",
        "\n",
        "write_data(VECTORIZERS + 'bow.pkl', fit_vectorizer(df['text'], method=CountVectorizer))\n",
        "write_data(VECTORIZERS + 'tf_idf.pkl', fit_vectorizer(df['text'], method=TfidfVectorizer))"
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "Create the representations (bow, td-idf, word embeddings) with the vectorizers we have for both the training set and the test set and store them for future use"
      ],
      "metadata": {
        "id": "QC2bao7lqE0m"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "write_data(VECTORIZERS + 'bow_train_vectorized.pkl', prepare_vector(df['text'], VECTORIZERS + 'bow.pkl'))\n",
        "write_data(VECTORIZERS + 'tf_idf_train_vectorized.pkl', prepare_vector(df['text'], VECTORIZERS + 'tf_idf.pkl'))\n",
        "write_data(VECTORIZERS + 'embeddings_train_vectorized.pkl', prepare_vector(df['text'], VECTORIZERS + 'embeddings.pkl'))"
      ],
      "metadata": {
        "id": "25VCyO5RoyPg"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "df = load_data(DATASETS + 'test.pkl')\n",
        "\n",
        "write_data(VECTORIZERS + 'bow_test_vectorized.pkl', prepare_vector(df['text'], VECTORIZERS + 'bow.pkl'))\n",
        "write_data(VECTORIZERS + 'tf_idf_test_vectorized.pkl', prepare_vector(df['text'], VECTORIZERS + 'tf_idf.pkl'))\n",
        "write_data(VECTORIZERS + 'embeddings_test_vectorized.pkl', prepare_vector(df['text'], VECTORIZERS + 'embeddings.pkl'))"
      ],
      "metadata": {
        "id": "HT-PEwgrp7xy"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "9kFjKsPp6fYr"
      },
      "source": [
        "# Question 4"
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "## Cross Validation and metrics analysis\n",
        "For (almost) default classifiers, do cross validation on a percentage of the dataset (5% in our case) and print the desired metrics using `classification_report`\n",
        "\n",
        "`classification_report` shows all the desired metrics, (both micro and macro precision, recall and f1)\n",
        "\n",
        "It also shows the results for each class seperately which is helpful and that's why it was chosen\n",
        "\n",
        "By default when `cv=int` `cross_validate` does K-Fold cross validation with `(Stratified)KFold` where K is the integer given"
      ],
      "metadata": {
        "id": "7HiTTET6Z-hq"
      }
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "dQZB87T545d-"
      },
      "outputs": [],
      "source": [
        "def cross_val(clf, X, y, factor, message = '', cv = 5):\n",
        "    start_time = time.time()\n",
        "    true = []\n",
        "    predicted = []\n",
        "\n",
        "    def cr_acc(y_true, y_pred):\n",
        "        true.extend(y_true)\n",
        "        predicted.extend(y_pred)\n",
        "        return accuracy_score(y_true, y_pred)\n",
        "        \n",
        "    cross_validate(estimator=clf, X=X, y=y, cv=cv, scoring=make_scorer(cr_acc))\n",
        "    \n",
        "    print(\"%s%s%.2f seconds, on %.2f%% of the given dataset - %d samples\" % \n",
        "    (message, ', ' if message != '' else '', time.time() - start_time, factor * 100, len(y)))\n",
        "\n",
        "    print(classification_report(true, predicted, zero_division=0))\n",
        "\n",
        "def train(dataset_path, fraction = 0.2, cv = 5):\n",
        "    \n",
        "    df = load_data(dataset_path).sample(frac=fraction)\n",
        "\n",
        "    y = df['sentiment']\n",
        "    bow = prepare_vector(df['text'], VECTORIZERS + 'bow.pkl')\n",
        "    tf_idf = prepare_vector(df['text'], VECTORIZERS + 'tf_idf.pkl')\n",
        "    emb = prepare_emb(df['text'], model_path=GLOVE + 'glove_200d.pkl')\n",
        "\n",
        "    svm_clf = svm.SVC(C = 100.)\n",
        "    rf_clf = RandomForestClassifier(max_depth=10, n_estimators=50)\n",
        "    knn_clf = KNeighborsClassifier()\n",
        "\n",
        "    cross_val(svm_clf, bow, y, fraction, message='SVM with BOW', cv=cv)\n",
        "    cross_val(svm_clf, tf_idf, y, fraction, message='SVM with Tf-idf', cv=cv)\n",
        "    cross_val(svm_clf, emb, y, fraction, message='SVM with Word Embeddings', cv=cv)\n",
        "\n",
        "    cross_val(rf_clf, bow, y, fraction, message='Random Forests with BOW', cv=cv)\n",
        "    cross_val(rf_clf, tf_idf, y, fraction, message='Random Forests with Tf-idf', cv=cv)\n",
        "    cross_val(rf_clf, emb, y, fraction, message='Random Forests with Word Embeddings', cv=cv)\n",
        "\n",
        "    cross_val(knn_clf, bow, y, fraction, message='KNN with BOW', cv=cv)\n",
        "    cross_val(knn_clf, tf_idf, y, fraction, message='KNN with Tf-idf', cv=cv)\n",
        "    cross_val(knn_clf, emb, y, fraction, message='KNN with Word Embeddings', cv=cv)"
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "train(DATASETS + 'train.pkl', 0.05, 10)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "Ek5vqMOJurOh",
        "outputId": "88425a00-4520-481e-d8fe-45e5b0c7a823"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "SVM with BOW, 61.74 seconds, on 5.00% of the given dataset - 9128 samples\n",
            "              precision    recall  f1-score   support\n",
            "\n",
            "         NEG       0.42      0.33      0.37      1258\n",
            "         NEU       0.82      0.89      0.85      6817\n",
            "         POS       0.57      0.42      0.48      1053\n",
            "\n",
            "    accuracy                           0.76      9128\n",
            "   macro avg       0.60      0.55      0.57      9128\n",
            "weighted avg       0.74      0.76      0.74      9128\n",
            "\n",
            "SVM with Tf-idf, 82.23 seconds, on 5.00% of the given dataset - 9128 samples\n",
            "              precision    recall  f1-score   support\n",
            "\n",
            "         NEG       0.55      0.30      0.39      1258\n",
            "         NEU       0.82      0.94      0.88      6817\n",
            "         POS       0.70      0.43      0.54      1053\n",
            "\n",
            "    accuracy                           0.79      9128\n",
            "   macro avg       0.69      0.56      0.60      9128\n",
            "weighted avg       0.77      0.79      0.77      9128\n",
            "\n",
            "SVM with Word Embeddings, 102.37 seconds, on 5.00% of the given dataset - 9128 samples\n",
            "              precision    recall  f1-score   support\n",
            "\n",
            "         NEG       0.59      0.55      0.57      1258\n",
            "         NEU       0.86      0.89      0.88      6817\n",
            "         POS       0.64      0.55      0.59      1053\n",
            "\n",
            "    accuracy                           0.81      9128\n",
            "   macro avg       0.70      0.67      0.68      9128\n",
            "weighted avg       0.80      0.81      0.80      9128\n",
            "\n",
            "Random Forests with BOW, 3.03 seconds, on 5.00% of the given dataset - 9128 samples\n",
            "              precision    recall  f1-score   support\n",
            "\n",
            "         NEG       0.75      0.00      0.01      1258\n",
            "         NEU       0.75      1.00      0.86      6817\n",
            "         POS       0.71      0.00      0.01      1053\n",
            "\n",
            "    accuracy                           0.75      9128\n",
            "   macro avg       0.74      0.34      0.29      9128\n",
            "weighted avg       0.74      0.75      0.64      9128\n",
            "\n",
            "Random Forests with Tf-idf, 2.04 seconds, on 5.00% of the given dataset - 9128 samples\n",
            "              precision    recall  f1-score   support\n",
            "\n",
            "         NEG       0.80      0.01      0.01      1258\n",
            "         NEU       0.75      1.00      0.86      6817\n",
            "         POS       0.75      0.00      0.01      1053\n",
            "\n",
            "    accuracy                           0.75      9128\n",
            "   macro avg       0.77      0.34      0.29      9128\n",
            "weighted avg       0.76      0.75      0.64      9128\n",
            "\n",
            "Random Forests with Word Embeddings, 41.09 seconds, on 5.00% of the given dataset - 9128 samples\n",
            "              precision    recall  f1-score   support\n",
            "\n",
            "         NEG       0.79      0.12      0.20      1258\n",
            "         NEU       0.77      0.99      0.87      6817\n",
            "         POS       0.85      0.13      0.23      1053\n",
            "\n",
            "    accuracy                           0.77      9128\n",
            "   macro avg       0.80      0.41      0.43      9128\n",
            "weighted avg       0.78      0.77      0.70      9128\n",
            "\n",
            "KNN with BOW, 2.19 seconds, on 5.00% of the given dataset - 9128 samples\n",
            "              precision    recall  f1-score   support\n",
            "\n",
            "         NEG       0.40      0.12      0.18      1258\n",
            "         NEU       0.77      0.96      0.86      6817\n",
            "         POS       0.66      0.16      0.25      1053\n",
            "\n",
            "    accuracy                           0.75      9128\n",
            "   macro avg       0.61      0.41      0.43      9128\n",
            "weighted avg       0.71      0.75      0.69      9128\n",
            "\n",
            "KNN with Tf-idf, 1.87 seconds, on 5.00% of the given dataset - 9128 samples\n",
            "              precision    recall  f1-score   support\n",
            "\n",
            "         NEG       0.43      0.12      0.18      1258\n",
            "         NEU       0.77      0.97      0.86      6817\n",
            "         POS       0.73      0.17      0.28      1053\n",
            "\n",
            "    accuracy                           0.76      9128\n",
            "   macro avg       0.64      0.42      0.44      9128\n",
            "weighted avg       0.72      0.76      0.70      9128\n",
            "\n",
            "KNN with Word Embeddings, 2.98 seconds, on 5.00% of the given dataset - 9128 samples\n",
            "              precision    recall  f1-score   support\n",
            "\n",
            "         NEG       0.50      0.41      0.45      1258\n",
            "         NEU       0.82      0.91      0.86      6817\n",
            "         POS       0.62      0.32      0.42      1053\n",
            "\n",
            "    accuracy                           0.77      9128\n",
            "   macro avg       0.65      0.55      0.58      9128\n",
            "weighted avg       0.75      0.77      0.76      9128\n",
            "\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "## Training models on the complete training set\n",
        "\n",
        "Train models on the complete training set, expect SVM with word embeddings (takes too much time), we train that on 35% of the training set\n",
        "\n",
        "* SVM -> BOW: 7h45min\n",
        "* SVM -> TF-IDF: 8h\n",
        "* SVM -> Word Embeddings: 3h35min (12+h, didn't finish) "
      ],
      "metadata": {
        "id": "dxN_EaCRaArk"
      }
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "VBu8anuA37KG"
      },
      "outputs": [],
      "source": [
        "def fit_save(Xs, y, clfs, *model_names):\n",
        "    names = [name for name in model_names]\n",
        "    i = 0\n",
        "    for clf in clfs:\n",
        "        for X in Xs:\n",
        "            print('Starting: ' + names[i])\n",
        "            model = clf.fit(X, y)\n",
        "            write_data(ROOT_PATH + names[i], model)\n",
        "            print('Finished: ' + names[i], end='\\n\\n')\n",
        "            i += 1"
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "def prepare(fraction=1.):\n",
        "    df = load_data(DATASETS + 'train.pkl').sample(frac=fraction)\n",
        "\n",
        "    y = df['sentiment']\n",
        "    bow = prepare_vector(df['text'], VECTORIZERS + 'bow.pkl')\n",
        "    tf_idf = prepare_vector(df['text'], VECTORIZERS + 'tf_idf.pkl')\n",
        "    emb = prepare_emb(df['text'], model_path=GLOVE + 'glove_200d.pkl')\n",
        "\n",
        "    svm_clf = svm.SVC(C = 100.)\n",
        "    rf_clf = RandomForestClassifier(max_depth=10, n_estimators=50)\n",
        "    knn_clf = KNeighborsClassifier()\n",
        "    return y, bow, tf_idf, emb, svm_clf, rf_clf, knn_clf"
      ],
      "metadata": {
        "id": "CD68NfKLW28U"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# y, bow, tf_idf, emb, svm_clf, rf_clf, knn_clf = prepare()\n",
        "# fit_save([bow], y, [svm_clf], 'svm_bow.pkl')\n",
        "# fit_save([tf_idf], y, [svm_clf], 'svm_tfidf.pkl')\n",
        "# fit_save([bow, tf_idf, emb], y, [rf_clf, knn_clf], 'rf_bow.pkl', 'rf_tfidf.pkl', 'rf_emb.pkl', 'knn_bow.pkl', 'knn_tfidf.pkl', 'knn_emb.pkl')\n",
        "\n",
        "\n",
        "y, bow, tf_idf, emb, svm_clf, rf_clf, knn_clf = prepare(0.35)\n",
        "fit_save([emb], y, [svm_clf], 'svm_emb.pkl')"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "1QwUgNO-W4o1",
        "outputId": "fccf6e58-2fc7-4714-838f-08918e392e59"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Starting: svm_emb.pkl\n",
            "Finished: svm_emb.pkl\n",
            "\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "## Evaluating fitted models on the test set"
      ],
      "metadata": {
        "id": "GjFZJc7Xroh8"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "def my_score(y_pred, y_test):\n",
        "    print(classification_report(y_test, y_pred, zero_division=0))\n",
        "\n",
        "def test(model_path, vectorized, y, message=''):\n",
        "    print(message)\n",
        "    clf = load_data(model_path)\n",
        "    y_pred = clf.predict(vectorized)\n",
        "    my_score(y_pred, y)"
      ],
      "metadata": {
        "id": "i1r_zO0erpUX"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "df = load_data(DATASETS + 'test.pkl')\n",
        "y = df['sentiment'].values\n",
        "bow = prepare_vector(df['text'], VECTORIZERS + 'bow.pkl')\n",
        "tf_idf = prepare_vector(df['text'], VECTORIZERS + 'tf_idf.pkl')\n",
        "emb = prepare_emb(df['text'], model_path=GLOVE + 'glove_200d.pkl')"
      ],
      "metadata": {
        "id": "PcEqVSUBr3m-"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "test(ROOT_PATH + 'svm_bow.pkl', bow, y, message='SVM BOW')"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "OCypUv-lr6dE",
        "outputId": "7c1c8382-2eb6-4819-ea51-14c721f6d390"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "SVM BOW\n",
            "              precision    recall  f1-score   support\n",
            "\n",
            "         NEG       0.52      0.44      0.48      6151\n",
            "         NEU       0.85      0.90      0.88     34248\n",
            "         POS       0.65      0.53      0.58      5243\n",
            "\n",
            "    accuracy                           0.80     45642\n",
            "   macro avg       0.67      0.62      0.65     45642\n",
            "weighted avg       0.78      0.80      0.79     45642\n",
            "\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "test(ROOT_PATH + 'svm_tfidf.pkl', tf_idf, y, message='SVM TF-IDF')"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "2mjI1fP8r6yC",
        "outputId": "2fdc165f-2a91-4e1e-f586-42e53a978d2c"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "SVM TF-IDF\n",
            "              precision    recall  f1-score   support\n",
            "\n",
            "         NEG       0.62      0.45      0.52      6151\n",
            "         NEU       0.86      0.93      0.89     34248\n",
            "         POS       0.71      0.56      0.63      5243\n",
            "\n",
            "    accuracy                           0.82     45642\n",
            "   macro avg       0.73      0.65      0.68     45642\n",
            "weighted avg       0.81      0.82      0.81     45642\n",
            "\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "test(ROOT_PATH + 'svm_emb.pkl', emb, y, message='SVM Word Embeddings')"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "i3dDccVquTNO",
        "outputId": "6a2b3ea9-824c-4fd3-d53c-214aaae75861"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "SVM Word Embeddings\n",
            "              precision    recall  f1-score   support\n",
            "\n",
            "         NEG       0.64      0.61      0.63      6151\n",
            "         NEU       0.88      0.91      0.89     34248\n",
            "         POS       0.69      0.61      0.65      5243\n",
            "\n",
            "    accuracy                           0.83     45642\n",
            "   macro avg       0.74      0.71      0.72     45642\n",
            "weighted avg       0.83      0.83      0.83     45642\n",
            "\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "test(ROOT_PATH + 'rf_bow.pkl', bow, y, message='\\nRandom Forests BOW')\n",
        "test(ROOT_PATH + 'rf_tfidf.pkl', tf_idf, y, message='\\nRandom Forests TF-IDF')\n",
        "test(ROOT_PATH + 'rf_emb.pkl', emb, y, message='\\nRandom Forests Word Embeddings')"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "_jK2EQQwr7CT",
        "outputId": "d5a962f1-b118-4ffa-8e44-3efe9887f37d"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "\n",
            "Random Forests BOW\n",
            "              precision    recall  f1-score   support\n",
            "\n",
            "         NEG       0.97      0.03      0.06      6151\n",
            "         NEU       0.75      1.00      0.86     34248\n",
            "         POS       1.00      0.00      0.01      5243\n",
            "\n",
            "    accuracy                           0.75     45642\n",
            "   macro avg       0.91      0.34      0.31     45642\n",
            "weighted avg       0.81      0.75      0.65     45642\n",
            "\n",
            "\n",
            "Random Forests TF-IDF\n",
            "              precision    recall  f1-score   support\n",
            "\n",
            "         NEG       1.00      0.02      0.05      6151\n",
            "         NEU       0.75      1.00      0.86     34248\n",
            "         POS       1.00      0.00      0.00      5243\n",
            "\n",
            "    accuracy                           0.75     45642\n",
            "   macro avg       0.92      0.34      0.30     45642\n",
            "weighted avg       0.81      0.75      0.65     45642\n",
            "\n",
            "\n",
            "Random Forests Word Embeddings\n",
            "              precision    recall  f1-score   support\n",
            "\n",
            "         NEG       0.87      0.13      0.23      6151\n",
            "         NEU       0.78      0.99      0.87     34248\n",
            "         POS       0.88      0.14      0.24      5243\n",
            "\n",
            "    accuracy                           0.78     45642\n",
            "   macro avg       0.84      0.42      0.45     45642\n",
            "weighted avg       0.80      0.78      0.71     45642\n",
            "\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "test(ROOT_PATH + 'knn_bow.pkl', bow, y, message='\\nK-NN BOW')\n",
        "test(ROOT_PATH + 'knn_tfidf.pkl', tf_idf, y, message='\\nK-NN TF-IDF')\n",
        "test(ROOT_PATH + 'knn_emb.pkl', emb, y, message='\\nK-NN Word Embeddings')"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "x5Pt4e6Fu2UH",
        "outputId": "3914e1e4-e85a-4379-e233-0c13adf7313f"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "\n",
            "K-NN BOW\n",
            "              precision    recall  f1-score   support\n",
            "\n",
            "         NEG       0.54      0.24      0.34      6151\n",
            "         NEU       0.80      0.95      0.87     34248\n",
            "         POS       0.71      0.27      0.39      5243\n",
            "\n",
            "    accuracy                           0.78     45642\n",
            "   macro avg       0.68      0.49      0.53     45642\n",
            "weighted avg       0.75      0.78      0.74     45642\n",
            "\n",
            "\n",
            "K-NN TF-IDF\n",
            "              precision    recall  f1-score   support\n",
            "\n",
            "         NEG       0.56      0.33      0.42      6151\n",
            "         NEU       0.82      0.94      0.87     34248\n",
            "         POS       0.70      0.38      0.49      5243\n",
            "\n",
            "    accuracy                           0.79     45642\n",
            "   macro avg       0.69      0.55      0.60     45642\n",
            "weighted avg       0.77      0.79      0.77     45642\n",
            "\n",
            "\n",
            "K-NN Word Embeddings\n",
            "              precision    recall  f1-score   support\n",
            "\n",
            "         NEG       0.58      0.52      0.54      6151\n",
            "         NEU       0.86      0.91      0.88     34248\n",
            "         POS       0.68      0.49      0.57      5243\n",
            "\n",
            "    accuracy                           0.81     45642\n",
            "   macro avg       0.71      0.64      0.67     45642\n",
            "weighted avg       0.80      0.81      0.80     45642\n",
            "\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "| Accuracy        | SVM  | Random Forests | K-NN |\n",
        "|-----------------|------|----------------|------|\n",
        "| BOW             | 0.80 | 0.75           | 0.78 |\n",
        "| TF-IDF          | 0.82 | 0.75           | 0.79 |\n",
        "| Word Embeddings | 0.83 | 0.78           | 0.81 |\n",
        "\n",
        "| F1-Score        | SVM  | Random Forests | K-NN |\n",
        "|-----------------|------|----------------|------|\n",
        "| BOW             | 0.65 | 0.31           | 0.53 |\n",
        "| TF-IDF          | 0.68 | 0.30           | 0.60 |\n",
        "| Word Embeddings | 0.72 | 0.45           | 0.67 |\n",
        "\n",
        "\n",
        "All models are trained on 100% of the training set (exept for SVM + Word Embeddings which is trained on 35% of it) and evaluated on 100% of the test set\n",
        "\n",
        "As expected the performance goes BOW < TF-IDF < Word Embeddings\n",
        "\n",
        "The word embeddings approach althought it gives the best results across all classifiers, it doesn't perform as better as we would expect. This is because the pretrained GLOVE model we use, is in fact trained on tweets, but lacks many covid related words, as well as some common words that appear in the documetns. Therefore since we manage such words by pretending they aren't a part of the tweet, we actually lose some meaning of the sentance. \n",
        "\n",
        "SVM is the best performing classifier, in theory and, as we see, in practice as well. Finding a hyperplane to lineary classify non lineary classifiable data should and does yield very good results in comparison to the other two methods used\n",
        "\n",
        "Ensembling methods are most likely not a good fit for the data and therefore give the worst results.  The nature (High variance, no additive structure) of decision trees in particular seems to be a problem that cannot be overcome with the techniques that constitute the Random Forests algorithm (Bagging, bootstrap aggregation etc). So we get results very close to the minimum (classifying everything as NEU would lead to a 75% accuracy) and this can be clearly seen by the recall and precision scores for positive and negative tweets, which in essence tells us that despite our predictions about them are (almost) always correct, they are not many and we misclassify a lot of positive and negative tweets as neutral. We can think of it as the model only classifying the \"extreme\" tweets and everything that is not that positive or negative to classify it as neutral.\n",
        "\n",
        "K-NN is a relatively simple and naive classifier, but it works decently for the classification problem in hand"
      ],
      "metadata": {
        "id": "D_yfuzaxvVlF"
      }
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "Sa0BqmTIZyYg"
      },
      "source": [
        "# Question 5"
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "Train models for different number of topics, choose the one with the largest topics' coherence and show it"
      ],
      "metadata": {
        "id": "Ju89Bid9sudT"
      }
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 1000
        },
        "id": "MeIegLumZ08V",
        "outputId": "bd4baea7-4fbe-49a2-9d82-190b3f157487"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Done for  5 topics, score: 0.3357, time: 1min10.93sec\n",
            "Done for  6 topics, score: 0.3726, time: 1min15.94sec\n",
            "Done for  7 topics, score: 0.3990, time: 1min18.74sec\n",
            "Done for  8 topics, score: 0.4024, time: 1min23.08sec\n",
            "Done for  9 topics, score: 0.3929, time: 1min23.77sec\n",
            "Done for 10 topics, score: 0.4195, time: 1min25.19sec\n",
            "Done for 11 topics, score: 0.4198, time: 1min26.93sec\n",
            "Done for 12 topics, score: 0.3863, time: 1min27.72sec\n",
            "Done for 13 topics, score: 0.3827, time: 1min26.66sec\n",
            "Done for 14 topics, score: 0.4041, time: 1min27.25sec\n",
            "Done for 15 topics, score: 0.3808, time: 1min30.48sec\n",
            "Done for 16 topics, score: 0.4150, time: 1min31.30sec\n",
            "Done for 17 topics, score: 0.3558, time: 1min33.37sec\n",
            "Done for 18 topics, score: 0.4121, time: 1min36.68sec\n",
            "Done for 19 topics, score: 0.3949, time: 1min35.67sec\n",
            "Done for 20 topics, score: 0.3680, time: 1min35.85sec\n",
            "Done for 21 topics, score: 0.3635, time: 1min38.12sec\n",
            "Done for 22 topics, score: 0.4015, time: 1min38.24sec\n",
            "Done for 23 topics, score: 0.3724, time: 1min40.50sec\n",
            "Done for 24 topics, score: 0.3636, time: 1min40.36sec\n",
            "Done for 25 topics, score: 0.3664, time: 1min42.18sec\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ],
            "image/png": "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\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "\n",
            "Number of topics: 11\n",
            "Coherence Score: 0.4198\n",
            "\n"
          ]
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "PreparedData(topic_coordinates=              x         y  topics  cluster       Freq\n",
              "topic                                                \n",
              "5      0.058326  0.147628       1        1  17.693451\n",
              "4     -0.413202  0.106366       2        1  13.806737\n",
              "9      0.179501  0.450913       3        1  10.787505\n",
              "0     -0.128221 -0.223631       4        1  10.755689\n",
              "1     -0.255593  0.360078       5        1   9.414248\n",
              "7     -0.172045  0.020433       6        1   8.159253\n",
              "3     -0.412592 -0.226811       7        1   6.981845\n",
              "10     0.385835 -0.067547       8        1   6.301594\n",
              "6      0.408525  0.247261       9        1   6.022670\n",
              "2      0.005638 -0.510129      10        1   5.381410\n",
              "8      0.343827 -0.304561      11        1   4.695597, topic_info=        Term          Freq         Total Category  logprob  loglift\n",
              "55       age  32194.000000  32194.000000  Default  10.0000  10.0000\n",
              "60      slot  33953.000000  33953.000000  Default   9.0000   9.0000\n",
              "110     dose  50953.000000  50953.000000  Default   8.0000   8.0000\n",
              "5     vaccin  74599.000000  74599.000000  Default   7.0000   7.0000\n",
              "448  covaxin  18831.000000  18831.000000  Default   6.0000   6.0000\n",
              "..       ...           ...           ...      ...      ...      ...\n",
              "97    clinic   1985.743646   4864.006980  Topic11  -3.7284   2.1627\n",
              "110     dose   3617.771234  50953.532034  Topic11  -3.1285   0.4135\n",
              "57    hospit   1961.297413  12438.857179  Topic11  -3.7408   1.2113\n",
              "979     road   1061.568116   2783.004555  Topic11  -4.3546   2.0948\n",
              "443   apollo   1055.940222   5150.075086  Topic11  -4.3599   1.4740\n",
              "\n",
              "[221 rows x 6 columns], token_table=      Topic      Freq       Term\n",
              "term                            \n",
              "865      11  0.999209     accept\n",
              "299       9  0.999494        add\n",
              "55        3  0.999928        age\n",
              "55        5  0.000062        age\n",
              "1282      3  0.999328        ago\n",
              "...     ...       ...        ...\n",
              "353       4  0.803997       year\n",
              "353       5  0.195907       year\n",
              "184       1  0.999972  yesterday\n",
              "137       5  0.000672        yet\n",
              "137       7  0.998890        yet\n",
              "\n",
              "[396 rows x 3 columns], R=10, lambda_step=0.01, plot_opts={'xlab': 'PC1', 'ylab': 'PC2'}, topic_order=[6, 5, 10, 1, 2, 8, 4, 11, 7, 3, 9])"
            ],
            "text/html": [
              "\n",
              "<link rel=\"stylesheet\" type=\"text/css\" href=\"https://cdn.jsdelivr.net/gh/bmabey/pyLDAvis@3.3.1/pyLDAvis/js/ldavis.v1.0.0.css\">\n",
              "\n",
              "\n",
              "<div id=\"ldavis_el581407010977887527643804679\"></div>\n",
              "<script type=\"text/javascript\">\n",
              "\n",
              "var ldavis_el581407010977887527643804679_data = {\"mdsDat\": {\"x\": [0.05832570867567253, -0.41320170503116155, 0.1795014381307309, -0.12822126004834603, -0.25559267051070517, -0.17204466380196384, -0.412592258631076, 0.38583488487057943, 0.4085250512819945, 0.0056381284874769825, 0.34382734657679836], \"y\": [0.14762826675365853, 0.10636592195072694, 0.4509131891412339, -0.22363121239672173, 0.3600776525229572, 0.020432928390700943, -0.22681117241298746, -0.06754701006324923, 0.24726139097892066, -0.5101290436046992, -0.30456091126054047], \"topics\": [1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11], \"cluster\": [1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1], \"Freq\": [17.693451322525082, 13.806737116095396, 10.787505366811727, 10.75568903373095, 9.414248362156147, 8.159252860812362, 6.981844620935888, 6.301594308006096, 6.022670000787474, 5.381409969811171, 4.695597038327705]}, \"tinfo\": {\"Term\": [\"age\", \"slot\", \"dose\", \"vaccin\", \"covaxin\", \"avail\", \"free\", \"pincod\", \"date\", \"fee\", \"got\", \"fulli\", \"second\", \"booster\", \"done\", \"yesterday\", \"shot\", \"concern\", \"johnson\", \"e\", \"first\", \"nd\", \"thank\", \"pfizer\", \"jab\", \"today\", \"st\", \"vaccin\", \"moderna\", \"get\", \"dose\", \"covid\", \"ocgn\", \"hope\", \"help\", \"possibl\", \"delta\", \"allow\", \"contact\", \"institut\", \"lead\", \"alreadi\", \"go\", \"want\", \"need\", \"let\", \"research\", \"sir\", \"way\", \"u\", \"pleas\", \"know\", \"would\", \"not\", \"take\", \"vaccin\", \"peopl\", \"get\", \"approv\", \"one\", \"pincod\", \"date\", \"fee\", \"r\", \"age\", \"sep\", \"aug\", \"oct\", \"jul\", \"ago\", \"rainbow\", \"covaxin\", \"slot\", \"apollo\", \"hospit\", \"dose\", \"man\", \"old\", \"die\", \"mix\", \"usa\", \"crore\", \"love\", \"market\", \"good\", \"ji\", \"two\", \"anoth\", \"offici\", \"import\", \"price\", \"v\", \"year\", \"sputnik\", \"vaccin\", \"receiv\", \"day\", \"covid\", \"health\", \"emerg\", \"biotech\", \"trial\", \"bharat\", \"use\", \"jayanagar\", \"child\", \"efficaci\", \"drug\", \"list\", \"phase\", \"approv\", \"data\", \"health\", \"india\", \"vaccin\", \"covaxin\", \"side\", \"far\", \"must\", \"columbia\", \"hurt\", \"asia\", \"noth\", \"person\", \"plan\", \"interest\", \"million\", \"effect\", \"donat\", \"suppli\", \"total\", \"boost\", \"do\", \"arriv\", \"china\", \"chines\", \"vaccin\", \"covid\", \"mask\", \"antibodi\", \"wear\", \"student\", \"team\", \"result\", \"tell\", \"symptom\", \"scienc\", \"head\", \"yet\", \"w\", \"ask\", \"not\", \"amp\", \"sec\", \"back\", \"issu\", \"life\", \"long\", \"delay\", \"risk\", \"medium\", \"save\", \"almost\", \"think\", \"watch\", \"heart\", \"drive\", \"cloudnin\", \"plot\", \"great\", \"free\", \"avail\", \"pm\", \"paid\", \"mumbai\", \"tmc\", \"uphc\", \"add\", \"appli\", \"thane\", \"sector\", \"marg\", \"shop\", \"ltd\", \"pain\", \"slot\", \"dose\", \"hospit\", \"pvt\", \"case\", \"report\", \"arm\", \"canada\", \"sore\", \"ontario\", \"open\", \"busi\", \"toronto\", \"embarrass\", \"hour\", \"forc\", \"new\", \"j\", \"pmc\", \"floor\", \"b\", \"check\", \"leader\", \"medicin\", \"credit\", \"join\", \"pune\", \"accept\", \"anti\", \"p\", \"build\", \"clinic\", \"dose\", \"hospit\", \"road\", \"apollo\"], \"Freq\": [32194.0, 33953.0, 50953.0, 74599.0, 18831.0, 10748.0, 10563.0, 11661.0, 11529.0, 10794.0, 12954.289773773153, 3720.229518754819, 8136.71396811937, 4122.5419587830875, 4848.17887251837, 2066.6411670678917, 11947.677171061378, 814.5195440046015, 936.8755563583757, 947.0127841980823, 12985.090893402823, 7095.2626371380875, 6041.538414166537, 3504.412319566176, 5433.881213618667, 8342.407568282302, 5538.693172688368, 29355.44927457518, 6176.5640039902755, 9478.894649077074, 17104.17200335537, 8816.556906930946, 3194.2989086791067, 1917.1727769372258, 1844.9377487900726, 1029.361596283059, 1087.826478532817, 1096.4821054842037, 729.602034858755, 723.366317780752, 753.3882362583107, 1534.826451252344, 4770.135302947695, 2702.0845565173836, 4466.187653546488, 2312.845068512822, 1308.2235971459593, 1224.5032461362878, 1406.9105289920615, 5940.559350425409, 2544.771746968261, 3144.089144663555, 2344.2768568714955, 8347.579178846876, 4128.227989288296, 12319.238128164227, 4045.343502234142, 5351.923316568825, 4895.832587935686, 3357.9144130352074, 11661.52374791352, 11528.835962547011, 10794.301614864726, 10574.54113883669, 32191.80911669835, 3733.4706493483777, 3489.814279212646, 2154.2374304625823, 1892.2409324281064, 869.3460534100112, 1007.9137576455872, 15525.19503802928, 21293.900992369832, 4093.616188362822, 6389.080577076316, 12424.095576949672, 2789.235433052779, 2532.056596739899, 2158.3505299565327, 1256.0130813324502, 1037.9397133060897, 811.1361586859106, 845.1262715594837, 902.0734797503636, 4147.936548678301, 822.8981533889314, 3105.964060819327, 1541.3672236929904, 1356.8110279804782, 1359.0321563900247, 1317.4699707792831, 3893.413441949026, 3829.3089314783138, 2949.93633418519, 17093.35157071103, 3858.835017812657, 3387.0962876577014, 3777.340467096286, 2816.1815872096595, 3452.300057514743, 3176.243840745916, 3536.8309742027996, 2937.1060631986484, 6954.22050185828, 1765.7273201412556, 2316.5224392379005, 2811.722661421514, 1648.4539343253805, 1572.1285381241146, 2176.0110589012675, 8469.922682830127, 2744.970398169572, 2941.4610078475052, 3339.751291377083, 5495.7864756013805, 3305.282952099682, 3735.615260853004, 1725.4367344125862, 906.8159165982223, 769.4479731038349, 654.3311903061418, 722.6378361877528, 596.6150086985994, 654.1135601666057, 907.0552094613251, 660.4480347226504, 3908.049470679993, 8172.616557472227, 1264.4839303082688, 1727.04472361561, 846.7841636780678, 858.095782694083, 8244.70207827133, 1848.1960458523097, 1911.3993151069935, 1443.1685044868364, 8625.43273562847, 2220.1099747177936, 1123.116749093261, 1195.0672491619375, 886.0800686076286, 923.5059312557672, 1167.5248899750184, 1260.436871040317, 1114.8865606907518, 776.5739488642664, 836.7124402464831, 685.6004851404542, 1486.6101193386733, 1588.8968739357135, 1458.776207326673, 3219.765912264508, 2284.503124779773, 1845.9901067137232, 1400.895134411329, 1140.754213935769, 1664.626330856544, 1012.2534408589254, 1139.007305696593, 1114.2005890461733, 701.4124719457545, 631.5356260002791, 599.9562447348544, 1835.567317868667, 848.367784016086, 763.1822281294089, 1063.9627509440297, 1135.6721206728892, 1063.1386109648026, 1354.3863748016577, 10562.970862744747, 10748.2746428363, 6254.984489925879, 4197.353296305479, 935.4206596895262, 712.4613305837363, 649.9634516015859, 555.0472815480855, 507.9984745096539, 582.0531142836234, 848.6701111041589, 880.5196671535165, 674.6446465602315, 779.1385532488439, 781.9915065767972, 12659.467524487261, 16894.57843020488, 3946.176142562894, 1008.3246523758742, 4275.013951862733, 4477.068587657296, 3409.8348751102126, 2999.176599140658, 2298.2761434196846, 2049.6291726471036, 1705.279566029385, 1656.961608614023, 1149.052963580841, 1099.6711104554877, 2186.271728975247, 1205.3082021213029, 1770.453695514411, 1767.7463972570506, 1493.617807531763, 1315.8272348688909, 2504.3711920483534, 966.5120200017992, 584.2800654482768, 642.6401858853204, 509.3141095963563, 627.9237951922796, 530.0427476393829, 762.1347033888569, 547.2453713168741, 2982.243987227779, 706.8133933856128, 1985.7436456119403, 3617.7712342711507, 1961.2974130125624, 1061.5681158677444, 1055.9402217063628], \"Total\": [32194.0, 33953.0, 50953.0, 74599.0, 18831.0, 10748.0, 10563.0, 11661.0, 11529.0, 10794.0, 12954.813559524831, 3720.611496853147, 8137.741208575461, 4123.235219239526, 4849.007201415906, 2067.057961159379, 11950.367997397458, 814.7534736463804, 937.1623204718377, 947.3515897281484, 13028.43663963729, 7097.966342611276, 6080.128053699715, 3513.6235777923016, 5522.519355045975, 8678.542462739792, 6219.678845287085, 74599.33135466318, 7374.530744873182, 15722.515522475416, 50953.53203359884, 17440.06030808354, 3194.7969098040244, 1917.6305936465433, 1845.4471737659085, 1029.6851427965078, 1088.1703377049118, 1096.8504658129311, 729.8850983118283, 723.6734871891416, 753.7126844892148, 1535.4902034150227, 4790.621664995154, 2704.975229010007, 4513.091085148537, 2325.692030445668, 1308.875267180328, 1225.1731557966327, 1411.3457370207614, 6630.531685212916, 2675.1838052859957, 3441.054772223742, 2471.9074625039393, 12044.014850889644, 5913.338337240495, 74599.33135466318, 7659.367811605614, 15722.515522475416, 13366.516904508708, 6763.9944489516565, 11661.837271237531, 11529.157439604272, 10794.612476456694, 10575.123870159872, 32194.312505832157, 3733.7703104060615, 3490.1333700656905, 2154.5456584895346, 1892.5185119394312, 869.5846241653778, 1008.2504320742377, 18831.673706348523, 33953.99590227414, 5150.075086366169, 12438.857179264629, 50953.53203359884, 2789.551336178209, 2532.385421024931, 2158.7287218039023, 1256.3202681067278, 1038.2209543362617, 811.3717970126411, 845.3802427358339, 902.3510819807242, 4149.215692448425, 823.1804066112835, 3120.471927263542, 1542.1184990004845, 1357.5908859721478, 1366.3066359761854, 1345.8049644025195, 4768.8835966232355, 4762.457332878283, 3799.131570324289, 74599.33135466318, 7421.05745447042, 7498.787229006035, 17440.06030808354, 5761.125763518397, 3452.6317065685284, 3176.5707578832, 3537.2024942595926, 2937.4356483964193, 6955.258808972266, 1765.9911666859214, 2316.889114399169, 2812.212937483623, 1648.8415258025939, 1572.516963097827, 2337.1445514811085, 13366.516904508708, 3433.47983391584, 5761.125763518397, 9103.182467729765, 74599.33135466318, 18831.673706348523, 3735.9684466796834, 1725.8830629362071, 907.0889628230511, 769.7329636281719, 654.5799641091171, 722.9236789555063, 596.8824330760825, 654.4097060963446, 907.4810062702816, 660.7712869811912, 3915.1327909111073, 8280.682615645332, 1265.6149175713256, 1740.5471097561497, 847.2988590666602, 858.8977245900094, 9470.796371178969, 2031.3314740004232, 3563.1037558165235, 2204.8662805613503, 74599.33135466318, 17440.06030808354, 1123.4488807129978, 1195.4259801225164, 886.3722559628139, 923.8152360970753, 1167.9310718402276, 1260.8843348601663, 1115.2904003387061, 776.8611169821163, 837.0240171294316, 685.8641375913253, 1488.6518584793864, 1593.913280324195, 1482.9866339702035, 12044.014850889644, 10751.765754473683, 1846.2774425517648, 1401.1515799198564, 1141.0703766227725, 1665.166747704191, 1012.5998455751018, 1139.428507829934, 1114.6444830241146, 701.7093596487606, 631.8029994091621, 600.21032364372, 1843.0467412385183, 848.7968854893926, 763.5347004992652, 1069.471592380271, 1145.7880242437766, 1075.406407529644, 2179.7218293347173, 10563.287038442964, 10748.656465461809, 6255.380141194968, 4197.63939369579, 935.725817748113, 712.7327711474115, 650.2325403609404, 555.2810642782838, 508.23066985255514, 582.3289100599518, 849.1372316803851, 881.8593359785021, 675.1554334973063, 781.9833457614028, 789.5842114339544, 33953.99590227414, 50953.53203359884, 12438.857179264629, 1279.3456007357252, 4275.381580750201, 4477.516185485284, 3410.1902777441783, 2999.528774801913, 2298.5990915412876, 2049.9198975956, 1705.5855424951067, 1657.2677330729573, 1149.329893864427, 1099.9396251818364, 2190.763272720415, 1205.6611898300628, 4225.479074485963, 1768.084608777001, 1493.939800100876, 1316.159553632651, 2505.265491704569, 966.8907239285052, 584.5279459563023, 642.9424588906354, 509.56299951413314, 628.2374528201558, 530.319659143873, 762.6035131010001, 547.5662455376348, 5149.361135384884, 768.032737476269, 4864.006979884011, 50953.53203359884, 12438.857179264629, 2783.0045553872487, 5150.075086366169], \"Category\": [\"Default\", \"Default\", \"Default\", \"Default\", \"Default\", \"Default\", \"Default\", \"Default\", \"Default\", \"Default\", \"Topic1\", \"Topic1\", \"Topic1\", \"Topic1\", \"Topic1\", \"Topic1\", \"Topic1\", \"Topic1\", \"Topic1\", \"Topic1\", \"Topic1\", \"Topic1\", \"Topic1\", \"Topic1\", \"Topic1\", \"Topic1\", \"Topic1\", \"Topic1\", \"Topic1\", \"Topic1\", \"Topic1\", \"Topic1\", \"Topic2\", \"Topic2\", \"Topic2\", \"Topic2\", \"Topic2\", \"Topic2\", \"Topic2\", \"Topic2\", \"Topic2\", \"Topic2\", \"Topic2\", \"Topic2\", \"Topic2\", \"Topic2\", \"Topic2\", \"Topic2\", \"Topic2\", \"Topic2\", \"Topic2\", \"Topic2\", \"Topic2\", \"Topic2\", \"Topic2\", \"Topic2\", \"Topic2\", \"Topic2\", \"Topic2\", \"Topic2\", \"Topic3\", \"Topic3\", \"Topic3\", \"Topic3\", \"Topic3\", \"Topic3\", \"Topic3\", \"Topic3\", \"Topic3\", \"Topic3\", \"Topic3\", \"Topic3\", \"Topic3\", \"Topic3\", \"Topic3\", \"Topic3\", \"Topic4\", \"Topic4\", \"Topic4\", \"Topic4\", \"Topic4\", \"Topic4\", \"Topic4\", \"Topic4\", \"Topic4\", \"Topic4\", \"Topic4\", \"Topic4\", \"Topic4\", \"Topic4\", \"Topic4\", \"Topic4\", \"Topic4\", \"Topic4\", \"Topic4\", \"Topic4\", \"Topic4\", \"Topic4\", \"Topic4\", \"Topic5\", \"Topic5\", \"Topic5\", \"Topic5\", \"Topic5\", \"Topic5\", \"Topic5\", \"Topic5\", \"Topic5\", \"Topic5\", \"Topic5\", \"Topic5\", \"Topic5\", \"Topic5\", \"Topic5\", \"Topic5\", \"Topic5\", \"Topic6\", \"Topic6\", \"Topic6\", \"Topic6\", \"Topic6\", \"Topic6\", \"Topic6\", \"Topic6\", \"Topic6\", \"Topic6\", \"Topic6\", \"Topic6\", \"Topic6\", \"Topic6\", \"Topic6\", \"Topic6\", \"Topic6\", \"Topic6\", \"Topic6\", \"Topic6\", \"Topic6\", \"Topic6\", \"Topic7\", \"Topic7\", \"Topic7\", \"Topic7\", \"Topic7\", \"Topic7\", \"Topic7\", \"Topic7\", \"Topic7\", \"Topic7\", \"Topic7\", \"Topic7\", \"Topic7\", \"Topic7\", \"Topic7\", \"Topic8\", \"Topic8\", \"Topic8\", \"Topic8\", \"Topic8\", \"Topic8\", \"Topic8\", \"Topic8\", \"Topic8\", \"Topic8\", \"Topic8\", \"Topic8\", \"Topic8\", \"Topic8\", \"Topic8\", \"Topic8\", \"Topic8\", \"Topic9\", \"Topic9\", \"Topic9\", \"Topic9\", \"Topic9\", \"Topic9\", \"Topic9\", \"Topic9\", \"Topic9\", \"Topic9\", \"Topic9\", \"Topic9\", \"Topic9\", \"Topic9\", \"Topic9\", \"Topic9\", \"Topic9\", \"Topic9\", \"Topic9\", \"Topic10\", \"Topic10\", \"Topic10\", \"Topic10\", \"Topic10\", \"Topic10\", \"Topic10\", \"Topic10\", \"Topic10\", \"Topic10\", \"Topic10\", \"Topic10\", \"Topic10\", \"Topic11\", \"Topic11\", \"Topic11\", \"Topic11\", \"Topic11\", \"Topic11\", \"Topic11\", \"Topic11\", \"Topic11\", \"Topic11\", \"Topic11\", \"Topic11\", \"Topic11\", \"Topic11\", \"Topic11\", \"Topic11\", \"Topic11\", \"Topic11\", \"Topic11\"], \"logprob\": [10.0, 9.0, 8.0, 7.0, 6.0, 5.0, 4.0, 3.0, 2.0, 1.0, -3.1795, -4.4272, -3.6446, -4.3245, -4.1623, -5.015, -3.2604, -5.9461, -5.8061, -5.7954, -3.1771, -3.7815, -3.9423, -4.4869, -4.0483, -3.6196, -4.0292, -2.3615, -3.9202, -3.4919, -2.9016, -3.5643, -4.3315, -4.8421, -4.8805, -5.464, -5.4087, -5.4008, -5.8082, -5.8167, -5.7761, -5.0645, -3.9305, -4.4989, -3.9964, -4.6544, -5.2242, -5.2904, -5.1515, -3.7111, -4.5589, -4.3474, -4.6409, -3.3709, -4.0751, -2.9817, -4.0953, -3.8154, -3.9045, -4.2816, -2.7898, -2.8013, -2.8671, -2.8877, -1.7744, -3.9288, -3.9963, -4.4787, -4.6084, -5.3862, -5.2383, -2.5037, -2.1877, -3.8367, -3.3915, -2.7265, -4.2174, -4.3142, -4.4738, -5.0152, -5.2059, -5.4525, -5.4115, -5.3462, -3.8206, -5.4381, -4.1099, -4.8105, -4.938, -4.9364, -4.9675, -3.8839, -3.9005, -4.1614, -2.4045, -3.8928, -4.0232, -3.9142, -4.2078, -3.8709, -3.9543, -3.8467, -4.0325, -3.1706, -4.5414, -4.2699, -4.0762, -4.6101, -4.6575, -4.3325, -2.9735, -4.1002, -4.0311, -3.9041, -3.406, -3.9145, -3.649, -4.4214, -5.0647, -5.229, -5.391, -5.2917, -5.4834, -5.3914, -5.0645, -5.3817, -3.6039, -2.8661, -4.7322, -4.4205, -5.1332, -5.1199, -2.8573, -4.3527, -4.3191, -4.6001, -2.8122, -4.1693, -4.695, -4.6329, -4.932, -4.8906, -4.6562, -4.5796, -4.7023, -5.0639, -4.9893, -5.1885, -4.4146, -4.348, -4.4335, -3.6418, -3.9849, -4.0955, -4.3714, -4.5769, -4.199, -4.6964, -4.5784, -4.6004, -5.0632, -5.1682, -5.2195, -4.1012, -4.873, -4.9788, -4.6466, -4.5813, -4.6473, -4.4052, -2.3059, -2.2885, -2.8299, -3.2288, -4.73, -5.0023, -5.0941, -5.252, -5.3406, -5.2045, -4.8274, -4.7905, -5.0568, -4.9128, -4.9092, -2.1249, -1.8363, -3.2905, -4.655, -3.0979, -3.0517, -3.324, -3.4524, -3.7185, -3.833, -4.017, -4.0457, -4.4118, -4.4557, -3.7685, -4.364, -3.9795, -3.8447, -4.0132, -4.1399, -3.4963, -4.4484, -4.9517, -4.8565, -5.0891, -4.8797, -5.0492, -4.686, -5.0172, -3.3217, -4.7614, -3.7284, -3.1285, -3.7408, -4.3546, -4.3599], \"loglift\": [10.0, 9.0, 8.0, 7.0, 6.0, 5.0, 4.0, 3.0, 2.0, 1.0, 1.7319, 1.7319, 1.7318, 1.7318, 1.7318, 1.7318, 1.7318, 1.7317, 1.7317, 1.7316, 1.7286, 1.7316, 1.7256, 1.7294, 1.7158, 1.6925, 1.616, 0.7993, 1.5547, 1.2259, 0.6404, 1.0498, 1.9799, 1.9798, 1.9797, 1.9797, 1.9797, 1.9797, 1.9796, 1.9796, 1.9796, 1.9796, 1.9757, 1.9789, 1.9696, 1.9745, 1.9795, 1.9795, 1.9769, 1.8701, 1.93, 1.8898, 1.927, 1.6134, 1.6207, 0.179, 1.3417, 0.9024, 0.9756, 1.2797, 2.2268, 2.2268, 2.2268, 2.2267, 2.2267, 2.2267, 2.2267, 2.2266, 2.2266, 2.2265, 2.2264, 2.0337, 1.7602, 1.9972, 1.5605, 0.8155, 2.2296, 2.2296, 2.2296, 2.2295, 2.2295, 2.2294, 2.2294, 2.2294, 2.2294, 2.2294, 2.2251, 2.2292, 2.2292, 2.2244, 2.2085, 2.0269, 2.0117, 1.9767, 0.7563, 1.5758, 1.435, 0.7, 1.514, 2.3628, 2.3628, 2.3628, 2.3628, 2.3628, 2.3628, 2.3628, 2.3628, 2.3627, 2.3627, 2.2915, 1.9067, 2.1391, 1.6907, 1.3602, -0.2452, 0.6229, 2.5059, 2.5058, 2.5057, 2.5056, 2.5056, 2.5056, 2.5056, 2.5056, 2.5055, 2.5055, 2.5042, 2.4929, 2.5051, 2.4982, 2.5054, 2.5051, 2.3674, 2.4115, 1.8832, 2.0822, 0.3486, 0.4448, 2.6616, 2.6616, 2.6615, 2.6615, 2.6615, 2.6615, 2.6615, 2.6615, 2.6615, 2.6615, 2.6605, 2.6587, 2.6454, 1.3426, 1.1129, 2.7642, 2.7642, 2.7641, 2.764, 2.764, 2.764, 2.764, 2.7639, 2.7639, 2.7639, 2.7603, 2.7639, 2.7639, 2.7592, 2.7555, 2.7529, 2.2885, 2.8096, 2.8096, 2.8096, 2.8096, 2.8093, 2.8093, 2.8092, 2.8092, 2.8092, 2.8092, 2.8091, 2.8081, 2.8089, 2.806, 2.8, 1.823, 1.7057, 1.6616, 2.5716, 2.9221, 2.9221, 2.9221, 2.9221, 2.9221, 2.9221, 2.922, 2.922, 2.922, 2.922, 2.9202, 2.9219, 2.0523, 3.0584, 3.0583, 3.0583, 3.0582, 3.0582, 3.0581, 3.0581, 3.0581, 3.058, 3.058, 3.0579, 3.058, 2.5123, 2.9755, 2.1627, 0.4135, 1.2113, 2.0948, 1.474]}, \"token.table\": {\"Topic\": [11, 9, 3, 5, 3, 2, 8, 2, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 4, 11, 7, 3, 11, 9, 2, 5, 10, 1, 4, 5, 6, 6, 2, 7, 3, 9, 11, 8, 5, 5, 6, 1, 9, 11, 10, 10, 10, 11, 5, 4, 5, 6, 1, 5, 6, 3, 5, 11, 1, 8, 6, 1, 2, 3, 5, 1, 2, 4, 5, 6, 7, 8, 10, 11, 11, 4, 2, 5, 7, 3, 1, 4, 6, 8, 10, 8, 2, 4, 1, 4, 6, 5, 6, 1, 1, 2, 3, 4, 6, 7, 8, 9, 10, 11, 8, 9, 5, 1, 1, 2, 4, 5, 6, 5, 10, 5, 6, 3, 1, 4, 11, 10, 9, 1, 1, 2, 4, 5, 6, 7, 8, 10, 11, 2, 8, 4, 7, 1, 2, 4, 8, 7, 4, 5, 7, 8, 2, 2, 1, 3, 9, 10, 11, 8, 10, 6, 1, 4, 7, 8, 2, 4, 5, 6, 2, 6, 8, 11, 1, 4, 6, 7, 8, 5, 4, 1, 11, 3, 1, 2, 6, 7, 8, 2, 11, 1, 2, 8, 8, 5, 8, 4, 3, 9, 4, 1, 9, 4, 7, 11, 8, 4, 6, 4, 1, 2, 4, 5, 6, 7, 8, 9, 6, 1, 9, 1, 2, 6, 7, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 1, 2, 4, 5, 6, 7, 8, 10, 11, 6, 2, 3, 4, 4, 1, 2, 4, 6, 7, 8, 10, 10, 3, 9, 11, 9, 1, 9, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 6, 1, 2, 6, 7, 5, 11, 3, 6, 1, 2, 7, 8, 1, 8, 9, 11, 2, 4, 6, 10, 11, 11, 3, 6, 9, 3, 3, 1, 4, 6, 10, 2, 7, 8, 3, 8, 9, 11, 8, 7, 8, 1, 9, 3, 9, 1, 10, 6, 2, 3, 9, 10, 3, 4, 1, 7, 9, 10, 11, 7, 6, 7, 7, 1, 2, 4, 6, 7, 8, 10, 11, 7, 7, 9, 1, 2, 8, 8, 11, 9, 1, 4, 7, 10, 10, 6, 5, 2, 4, 1, 2, 5, 7, 9, 4, 5, 3, 4, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 3, 7, 2, 6, 8, 1, 2, 7, 1, 2, 7, 4, 5, 1, 5, 7], \"Freq\": [0.9992086148429267, 0.9994938342105198, 0.9999281703614035, 6.212277400356632e-05, 0.9993276972141281, 0.9992246292093236, 0.9996495834286169, 0.9996807511933762, 0.23949554508556634, 0.293347164737816, 9.300797867400634e-05, 0.06389648134904236, 0.026879305836787833, 0.04575992550761112, 0.21252323127010447, 0.07384833506716103, 0.00018601595734801267, 0.008835757974030602, 0.03506400796010039, 0.9992746997061449, 0.9989658866990991, 0.9996436583028983, 0.7949398661852671, 0.20504555415037665, 0.9995461315771792, 0.3662883932274468, 0.6336729351790183, 0.999944203188479, 0.0004922879465017296, 0.08861183037031134, 0.0009845758930034593, 0.9097481251351964, 1.0001055727550714, 0.016183557862384762, 0.983825455050807, 0.9999617865417882, 0.9999389258123638, 0.9994948672271425, 0.9998918176148615, 0.9998516909139245, 0.9998203226288024, 0.9989547945415295, 0.9999429527477772, 0.0794236977455467, 0.9205336771492052, 0.9998384491125879, 0.9998237140425672, 0.9999107493113788, 1.0001130180161941, 1.000047859692612, 0.21582307243920698, 0.247536995957582, 0.5363301579080944, 0.0004535422437252766, 0.3446921052312102, 0.6544614576955741, 0.2534947020222826, 0.33819852783994714, 0.40830533513078116, 0.008727617838910498, 0.9914573865002327, 0.9990477689500044, 1.0003025778491208, 1.0001574243513636, 0.8244089315739482, 0.17550219123039607, 0.5055601783620717, 0.0038990690857004504, 0.2165703520101559, 0.14535500194486237, 0.12729313779786763, 0.0002866962563015037, 0.0009174280201648118, 5.733925126030074e-05, 5.733925126030074e-05, 0.9988951326633411, 0.9995417673944177, 0.0002912497082761406, 0.7994804492180059, 0.20008854958570857, 0.999986344222889, 0.4958135077654179, 0.4516730367943707, 0.0016002587663219512, 0.003333872429837398, 0.04747434340088455, 0.9996239274101101, 0.999843464116775, 0.9996624301161411, 0.0001055877416014505, 0.12934498346177686, 0.8705709295039593, 0.0007901297512508527, 0.9987240055810779, 0.9997922870859809, 0.33567839789244824, 1.962572485339384e-05, 0.24383000557856507, 1.962572485339384e-05, 1.962572485339384e-05, 1.962572485339384e-05, 0.017859409616588395, 0.3315766213980889, 1.962572485339384e-05, 0.07100587251957892, 0.9948838357004948, 0.004675205994833152, 0.9994896260256521, 0.9996288709155496, 0.0002415259819548265, 0.011230958160899434, 0.001328392900751546, 0.00012076299097741325, 0.9869959252583985, 0.9999242811663424, 1.0000548892109906, 0.9998170362140489, 0.9994883413858267, 0.9999432609129758, 0.9966660129040241, 0.0032237175619537167, 0.9998787733354891, 0.9994515956591785, 0.9999728267875407, 0.999835646142129, 0.6028933465799237, 0.3404035437172435, 0.0039433893330472905, 0.042168824642102476, 0.0001908091612764818, 0.002925740472906054, 0.0073143511822651355, 6.360305375882726e-05, 6.360305375882726e-05, 0.9956954093983594, 0.0041748235194899765, 0.9997070066878814, 0.00024100940373381904, 0.9999372002135659, 0.37390092122385626, 0.004587741364709893, 0.6211801807817194, 1.000198089390053, 0.48879335664428036, 0.5104905049328227, 0.0003471543726166764, 0.999299703734597, 0.9997576881244474, 0.99967115999889, 0.011255053256289308, 0.513632394674517, 0.31723171535226863, 8.039323754492363e-05, 0.15765113882559523, 0.0018258476622318653, 0.9978257474097144, 0.9991139904352153, 0.0007319001267131589, 0.994652272203183, 0.0014638002534263178, 0.0029276005068526356, 0.32944522540674925, 0.30297096754645364, 0.36690465250368204, 0.0005492584618318594, 0.9990693493667185, 0.9988327474326026, 0.9999383240296004, 0.9999521466469529, 0.9839712005780307, 0.0005432303278862886, 0.00036215355192419237, 0.012313220765422541, 0.002535074863469347, 1.0000050019016207, 0.9997808419517343, 0.9998267957766847, 0.9996220333265872, 0.9997260201492562, 0.01075251701852138, 0.9136733380062493, 0.002615477112613309, 0.06945544776828676, 0.0029060856806814544, 0.9990544347947418, 0.9990967994602232, 0.0021498977227186263, 0.9945426865296366, 0.0030098568118060768, 0.9998998612574861, 0.9996712511788689, 0.9994076183423067, 0.9995502109978306, 0.002557599226173592, 0.996184898594614, 0.9998023566832921, 0.0011339676966626545, 0.9990255407597985, 0.9996109252953368, 0.9996004440249093, 1.0000894965149196, 0.9989890976384929, 0.001787934247402883, 0.9981781484072096, 0.9997450744727613, 0.8376126174935656, 0.00040680554516443204, 0.00013560184838814403, 0.00013560184838814403, 0.16109499588511508, 0.00027120369677628806, 0.0005424073935525761, 0.9992243264700554, 0.9999019249194983, 0.9995820855625268, 0.00028177084864341843, 0.000443155248202613, 0.9895656692364349, 0.0002215776241013065, 0.00952783783635618, 0.023192636449612964, 0.1223529902494888, 0.00023665955560829555, 0.23287300271856284, 0.042598720009493204, 0.004733191112165911, 0.0802275893512122, 0.03833884800854388, 0.03620891200806922, 0.4188874134266831, 0.00023665955560829555, 0.0005812015417336469, 0.6931243529132121, 0.013284606668197642, 8.302879167623527e-05, 0.020092967585648935, 0.26735270919747756, 0.0048986987088978804, 8.302879167623527e-05, 0.0004151439583811763, 1.0001969683096745, 0.9997505601055332, 0.9997467408094209, 0.9995647540225459, 0.9998478031733514, 0.34816705095980655, 0.4964522110925819, 0.11605568365326885, 0.031638109938598134, 0.002069782893179317, 0.00547014050340248, 1.0000390758704738, 0.9996566912180493, 0.25420629192327177, 0.16642841266481584, 0.579100964488309, 0.9998476777932019, 0.008865425496904738, 0.9903946769399292, 0.045304000086563184, 0.5281114707497062, 0.00013055907805925988, 0.11841708379974872, 0.0009139135464148192, 0.06306003470262253, 0.1109752163503709, 0.13264802330820805, 0.00013055907805925988, 0.00013055907805925988, 0.00013055907805925988, 0.9993739302878183, 0.9972610675050319, 0.0008538194071104725, 0.0008538194071104725, 0.0005692129380736483, 0.9310506697675216, 0.06888748062158594, 1.0000139539558548, 0.9994699544486794, 0.006728509631537514, 0.9513365006812763, 0.040744863879866054, 0.000747612181281946, 0.011158572160236283, 0.9884635171942641, 0.999939229721234, 1.0000402960675656, 0.9993346094179363, 0.9785964793083464, 0.0037152485926664628, 0.0007430497185332926, 0.016347093807732437, 0.999397233086948, 0.0023449488537536393, 0.2094820976019918, 0.7879028148612228, 0.999988286646909, 0.9997516171912543, 0.41503519126436866, 0.5200067542497399, 0.06481556071368874, 0.9998847161095794, 0.9993312829707498, 0.9992986391886102, 0.9994218039617744, 0.2594318426832526, 0.22493675002730765, 0.1340278079236194, 0.3816019625063909, 1.0003118069889223, 0.9999713065229431, 0.9998497286781659, 0.9999089171606145, 0.999838386923497, 0.999793691003457, 0.9997697811650554, 0.9998018473240344, 8.367943148008323e-05, 1.0000084458208807, 0.9998586683067504, 0.6271426803869582, 0.3728279886831269, 0.9997393666675097, 0.22320890558881482, 0.7764932446780705, 0.8905604513964793, 0.00016078000566825768, 0.00016078000566825768, 0.00016078000566825768, 0.10884806383741046, 1.0002000009262733, 0.9922167520314646, 0.007468915909906798, 1.0001787745773958, 0.24791410813880813, 0.6980828365600272, 0.011330317356957807, 0.0006764368571318094, 0.0011837644999806664, 0.03906422849936199, 0.0005073276428488571, 0.0008455460714147618, 1.00005901731826, 0.999739619081615, 0.9994351816400152, 0.9937290706111832, 0.0057564577079429686, 0.0004934106606808258, 0.9961765802891234, 0.0037980588573114727, 0.9989718851481575, 0.961221315193802, 0.0009218137762587408, 0.0003456801660970278, 0.037218231216446664, 0.9997129685165346, 0.9996472802205949, 0.99994275299197, 0.004166036517239296, 0.9953622632727117, 0.001357357211650365, 0.8960065771572021, 0.10029361619416587, 0.002262262019417275, 0.999642373540993, 0.9997871798528638, 0.9998190133527969, 0.18348109830560186, 0.8163336179470949, 0.3935021865067297, 0.1651355283793699, 2.680989177358063e-05, 0.22913074004290684, 0.07367358259379957, 0.11561765827356646, 0.014919704771997621, 0.007694438939017641, 4.021483766037094e-05, 0.00014745440475469345, 8.042967532074189e-05, 0.0025095468174946237, 0.9969174732497393, 0.998900090108738, 0.0007393783050397765, 0.9990611587966265, 0.0028341744301744816, 0.9969208558138739, 0.9995800229978886, 0.0004045458882134049, 0.948255561972221, 0.050568236026675616, 0.8039967042992635, 0.19590726693946536, 0.9999719595867809, 0.0006717487331265419, 0.9988903661591678], \"Term\": [\"accept\", \"add\", \"age\", \"age\", \"ago\", \"allow\", \"almost\", \"alreadi\", \"amp\", \"amp\", \"amp\", \"amp\", \"amp\", \"amp\", \"amp\", \"amp\", \"amp\", \"amp\", \"amp\", \"anoth\", \"anti\", \"antibodi\", \"apollo\", \"apollo\", \"appli\", \"approv\", \"approv\", \"arm\", \"arriv\", \"arriv\", \"arriv\", \"arriv\", \"asia\", \"ask\", \"ask\", \"aug\", \"avail\", \"b\", \"back\", \"bharat\", \"biotech\", \"boost\", \"booster\", \"build\", \"build\", \"busi\", \"canada\", \"case\", \"check\", \"child\", \"china\", \"china\", \"china\", \"chines\", \"chines\", \"chines\", \"clinic\", \"clinic\", \"clinic\", \"cloudnin\", \"cloudnin\", \"columbia\", \"concern\", \"contact\", \"covaxin\", \"covaxin\", \"covid\", \"covid\", \"covid\", \"covid\", \"covid\", \"covid\", \"covid\", \"covid\", \"covid\", \"credit\", \"crore\", \"data\", \"data\", \"data\", \"date\", \"day\", \"day\", \"day\", \"day\", \"day\", \"delay\", \"delta\", \"die\", \"do\", \"do\", \"do\", \"donat\", \"donat\", \"done\", \"dose\", \"dose\", \"dose\", \"dose\", \"dose\", \"dose\", \"dose\", \"dose\", \"dose\", \"dose\", \"drive\", \"drive\", \"drug\", \"e\", \"effect\", \"effect\", \"effect\", \"effect\", \"effect\", \"efficaci\", \"embarrass\", \"emerg\", \"far\", \"fee\", \"first\", \"first\", \"floor\", \"forc\", \"free\", \"fulli\", \"get\", \"get\", \"get\", \"get\", \"get\", \"get\", \"get\", \"get\", \"get\", \"go\", \"go\", \"good\", \"good\", \"got\", \"great\", \"great\", \"great\", \"head\", \"health\", \"health\", \"health\", \"heart\", \"help\", \"hope\", \"hospit\", \"hospit\", \"hospit\", \"hospit\", \"hospit\", \"hour\", \"hour\", \"hurt\", \"import\", \"import\", \"import\", \"import\", \"india\", \"india\", \"india\", \"india\", \"institut\", \"interest\", \"issu\", \"j\", \"jab\", \"jab\", \"jab\", \"jab\", \"jab\", \"jayanagar\", \"ji\", \"johnson\", \"join\", \"jul\", \"know\", \"know\", \"know\", \"know\", \"know\", \"lead\", \"leader\", \"let\", \"let\", \"let\", \"life\", \"list\", \"long\", \"love\", \"ltd\", \"ltd\", \"man\", \"marg\", \"marg\", \"market\", \"mask\", \"medicin\", \"medium\", \"million\", \"million\", \"mix\", \"moderna\", \"moderna\", \"moderna\", \"moderna\", \"moderna\", \"moderna\", \"moderna\", \"mumbai\", \"must\", \"nd\", \"nd\", \"need\", \"need\", \"need\", \"need\", \"new\", \"new\", \"new\", \"new\", \"new\", \"new\", \"new\", \"new\", \"new\", \"new\", \"new\", \"not\", \"not\", \"not\", \"not\", \"not\", \"not\", \"not\", \"not\", \"not\", \"noth\", \"ocgn\", \"oct\", \"offici\", \"old\", \"one\", \"one\", \"one\", \"one\", \"one\", \"one\", \"ontario\", \"open\", \"p\", \"p\", \"p\", \"paid\", \"pain\", \"pain\", \"peopl\", \"peopl\", \"peopl\", \"peopl\", \"peopl\", \"peopl\", \"peopl\", \"peopl\", \"peopl\", \"peopl\", \"peopl\", \"person\", \"pfizer\", \"pfizer\", \"pfizer\", \"pfizer\", \"phase\", \"phase\", \"pincod\", \"plan\", \"pleas\", \"pleas\", \"pleas\", \"pleas\", \"plot\", \"plot\", \"pm\", \"pmc\", \"possibl\", \"price\", \"price\", \"price\", \"price\", \"pune\", \"pvt\", \"pvt\", \"pvt\", \"r\", \"rainbow\", \"receiv\", \"receiv\", \"receiv\", \"report\", \"research\", \"result\", \"risk\", \"road\", \"road\", \"road\", \"road\", \"save\", \"scienc\", \"sec\", \"second\", \"sector\", \"sep\", \"shop\", \"shot\", \"shot\", \"side\", \"sir\", \"slot\", \"slot\", \"sore\", \"sputnik\", \"sputnik\", \"st\", \"st\", \"st\", \"st\", \"st\", \"student\", \"suppli\", \"suppli\", \"symptom\", \"take\", \"take\", \"take\", \"take\", \"take\", \"take\", \"take\", \"take\", \"team\", \"tell\", \"thane\", \"thank\", \"thank\", \"thank\", \"think\", \"think\", \"tmc\", \"today\", \"today\", \"today\", \"today\", \"toronto\", \"total\", \"trial\", \"two\", \"two\", \"u\", \"u\", \"u\", \"u\", \"uphc\", \"usa\", \"use\", \"v\", \"v\", \"vaccin\", \"vaccin\", \"vaccin\", \"vaccin\", \"vaccin\", \"vaccin\", \"vaccin\", \"vaccin\", \"vaccin\", \"vaccin\", \"vaccin\", \"w\", \"w\", \"want\", \"want\", \"watch\", \"way\", \"way\", \"wear\", \"would\", \"would\", \"would\", \"year\", \"year\", \"yesterday\", \"yet\", \"yet\"]}, \"R\": 10, \"lambda.step\": 0.01, \"plot.opts\": {\"xlab\": \"PC1\", \"ylab\": \"PC2\"}, \"topic.order\": [6, 5, 10, 1, 2, 8, 4, 11, 7, 3, 9]};\n",
              "\n",
              "function LDAvis_load_lib(url, callback){\n",
              "  var s = document.createElement('script');\n",
              "  s.src = url;\n",
              "  s.async = true;\n",
              "  s.onreadystatechange = s.onload = callback;\n",
              "  s.onerror = function(){console.warn(\"failed to load library \" + url);};\n",
              "  document.getElementsByTagName(\"head\")[0].appendChild(s);\n",
              "}\n",
              "\n",
              "if(typeof(LDAvis) !== \"undefined\"){\n",
              "   // already loaded: just create the visualization\n",
              "   !function(LDAvis){\n",
              "       new LDAvis(\"#\" + \"ldavis_el581407010977887527643804679\", ldavis_el581407010977887527643804679_data);\n",
              "   }(LDAvis);\n",
              "}else if(typeof define === \"function\" && define.amd){\n",
              "   // require.js is available: use it to load d3/LDAvis\n",
              "   require.config({paths: {d3: \"https://d3js.org/d3.v5\"}});\n",
              "   require([\"d3\"], function(d3){\n",
              "      window.d3 = d3;\n",
              "      LDAvis_load_lib(\"https://cdn.jsdelivr.net/gh/bmabey/pyLDAvis@3.3.1/pyLDAvis/js/ldavis.v3.0.0.js\", function(){\n",
              "        new LDAvis(\"#\" + \"ldavis_el581407010977887527643804679\", ldavis_el581407010977887527643804679_data);\n",
              "      });\n",
              "    });\n",
              "}else{\n",
              "    // require.js not available: dynamically load d3 & LDAvis\n",
              "    LDAvis_load_lib(\"https://d3js.org/d3.v5.js\", function(){\n",
              "         LDAvis_load_lib(\"https://cdn.jsdelivr.net/gh/bmabey/pyLDAvis@3.3.1/pyLDAvis/js/ldavis.v3.0.0.js\", function(){\n",
              "                 new LDAvis(\"#\" + \"ldavis_el581407010977887527643804679\", ldavis_el581407010977887527643804679_data);\n",
              "            })\n",
              "         });\n",
              "}\n",
              "</script>"
            ]
          },
          "metadata": {},
          "execution_count": 22
        }
      ],
      "source": [
        "LDA_PATH = ROOT_PATH + 'lda/'\n",
        "\n",
        "def model_coherence(corpus, dictionary, data, k, a='auto', b='auto'):\n",
        "    start_time = time.time()\n",
        "    lda_model = LdaModel(corpus=corpus,\n",
        "                id2word=dictionary,\n",
        "                num_topics=k, \n",
        "                random_state=100,\n",
        "                chunksize=2000,\n",
        "                alpha=a,\n",
        "                eta=b)\n",
        "    \n",
        "    coherence_model = CoherenceModel(model=lda_model, texts=data, dictionary=dictionary, coherence='c_v')\n",
        "    vis = gensimvis.prepare(lda_model, corpus, dictionary, mds=\"mmds\", R=10)\n",
        "    time_taken = time.time() - start_time\n",
        "    return vis, coherence_model.get_coherence(), time_taken // 60, time_taken % 60\n",
        "\n",
        "\n",
        "def lda(path, frac=1., start=5, topics=10):\n",
        "\n",
        "    df = load_data(path).sample(frac=frac, random_state=100)\n",
        "\n",
        "    # tokenize and clean data\n",
        "    tokenized_tweets = [word_tokenize(tweet) for tweet in df['text']]\n",
        "\n",
        "    # lemmatization\n",
        "    lemmatizer = WordNetLemmatizer()\n",
        "    lemmatized_tweets = [[lemmatizer.lemmatize(token) for token in tweet] for tweet in tokenized_tweets] \n",
        "\n",
        "    # stemming\n",
        "    stemmer = PorterStemmer()\n",
        "    stemmed_tweets = [[stemmer.stem(token) for token in tweet] for tweet in lemmatized_tweets]  \n",
        "\n",
        "    # bow,create a corpus from a list of texts\n",
        "    dictionary = Dictionary(stemmed_tweets)\n",
        "    corpus = [dictionary.doc2bow(text) for text in stemmed_tweets]\n",
        "\n",
        "    # lda, train the model on the corpus.\n",
        "    results = []\n",
        "    for t in range(start,start + topics + 1):\n",
        "        vis, coherence_lda, min, sec = model_coherence(corpus, dictionary, stemmed_tweets, t)\n",
        "        print(\"Done for %2d topics, score: %.4f, time: %dmin%.2fsec\" % (t, coherence_lda, min, sec))\n",
        "        results.append((vis, t, coherence_lda))\n",
        "        write_data(LDA_PATH + 'lda_result_' + str(t) + '_' + str(frac) + '.pkl', vis)\n",
        "    \n",
        "\n",
        "    results.sort(reverse=False, key=lambda x: x[1])\n",
        "    line_plot([x[1] for x in results], [x[2] for x in results], '# of topics x topic coherence')\n",
        "\n",
        "    results.sort(reverse=True, key=lambda x: x[2])\n",
        "    print('\\nNumber of topics: %d\\nCoherence Score: %.4f\\n' % (results[0][1], results[0][2]))\n",
        "    \n",
        "    return results[0][0]\n",
        "  \n",
        "pyLDAvis.enable_notebook()\n",
        "lda(DATASETS + 'data_set_clean.pkl', topics=20)"
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "# Question 6"
      ],
      "metadata": {
        "id": "53TNGnxxt-f7"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "## Fine tuning to find the best parameters for the classifiers"
      ],
      "metadata": {
        "id": "QlaVswI1_3p-"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "def train(dataset_path, fraction = 0.01, cv = 10):\n",
        "    \n",
        "    df = load_data(dataset_path).sample(frac=fraction, random_state=100) \n",
        "\n",
        "    X = df['text'].values\n",
        "    y = df['sentiment'].values\n",
        "\n",
        "    X_train, X_test, y_train, y_test = train_test_split(X, y, test_size = 0.20, random_state = 101)\n",
        "\n",
        "    bvec = fit_vectorizer(X_train, method=CountVectorizer)\n",
        "    tfvec = fit_vectorizer(X_train, method=TfidfVectorizer)\n",
        "\n",
        "    bow_train = transform_data(X_train, bvec)\n",
        "    tf_idf_train = transform_data(X_train, tfvec)\n",
        "    emb_train = prepare_emb(X_train, model_path=GLOVE + 'glove_200d.pkl')\n",
        "\n",
        "    bow_test= transform_data(X_test, bvec)\n",
        "    tf_idf_test = transform_data(X_test, tfvec)\n",
        "    emb_test = prepare_emb(X_test, model_path=GLOVE + 'glove_200d.pkl')\n",
        "\n",
        "\n",
        "    scoring = { 'accuracy' : make_scorer(accuracy_score), \n",
        "                'precision' : make_scorer(precision_score, average='micro'),\n",
        "                'recall' : make_scorer(recall_score, average='micro'), \n",
        "                'f1_score' : make_scorer(f1_score, average='micro')\n",
        "                }\n",
        "                \n",
        "    # default: C=1. gamma='scale', degree=3, kernel='rbf'\n",
        "    param_grid_svm = [  {'C': [1., 10., 100., 200.], 'gamma': [0.001, 0.0001, 'scale'], 'kernel': ['rbf']},\n",
        "                        {'C': [1., 10., 100., 200.], 'gamma': [0.001, 0.0001, 'scale'], 'degree': [3, 4, 5], 'kernel': ['poly']}                        \n",
        "                    ]\n",
        "    # default: max_depth=None, n_estimators=100, max_features='auto'\n",
        "    param_grid_rf = [   {'max_depth': [10, 20, 40, 100], 'n_estimators': [10, 50, 100, 200, 500], 'max_features': ['auto', 'sqrt', 'log2']},                      \n",
        "                    ]\n",
        "    # default: n_neighborsint=5, p=2, weights='uniform'\n",
        "    param_grid_knn = [  {'n_neighbors': [1, 3, 5, 10, 20], 'p': [1, 2], 'weights': ['uniform', 'distance']},               \n",
        "                    ]\n",
        "                    \n",
        "    def fine_tune(clf, param_grid, X_train, y_train, X_test, y_test, cv=10, refit=True, scoring=None, verbose=0, message=''):\n",
        "        start_time = time.time()\n",
        "\n",
        "        # grid = GridSearchCV(clf, param_grid, cv=cv, refit=refit, scoring=scoring, verbose=verbose).fit(X_train, y_train)\n",
        "        grid = RandomizedSearchCV(clf, param_grid, cv=cv, refit=refit, scoring=scoring, verbose=verbose, n_jobs=-1, n_iter=50).fit(X_train, y_train)\n",
        "\n",
        "        to_print = (message + ' took') if message != '' else 'Took'\n",
        "        print(to_print + ' %.2f seconds' % (time.time() - start_time))\n",
        "\n",
        "        print(grid.best_params_)\n",
        "        print(grid.best_estimator_)\n",
        "\n",
        "        grid_predictions = grid.predict(X_test)\n",
        "        print(classification_report(y_test, grid_predictions, zero_division=0))\n",
        "\n",
        "    fine_tune(svm.SVC(), param_grid_svm, bow_train, y_train, bow_test, y_test, cv=cv, message='\\nSVM BOW')\n",
        "    fine_tune(svm.SVC(), param_grid_svm, tf_idf_train, y_train, tf_idf_test, y_test, cv=cv, message='\\nSVM TF-IDF')\n",
        "    fine_tune(svm.SVC(), param_grid_svm, emb_train, y_train, emb_test, y_test, cv=cv, message='\\nSVM EMBEDDINGS')\n",
        "\n",
        "    fine_tune(RandomForestClassifier(), param_grid_rf, bow_train, y_train, bow_test, y_test, cv=cv, message='\\nRF BOW')\n",
        "    fine_tune(RandomForestClassifier(), param_grid_rf, tf_idf_train, y_train, tf_idf_test, y_test, cv=cv, message='\\nRF TF-IDF')\n",
        "    fine_tune(RandomForestClassifier(), param_grid_rf, emb_train, y_train, emb_test, y_test, cv=cv, message='\\nRF EMBEDDINGS')\n",
        "\n",
        "    fine_tune(KNeighborsClassifier(), param_grid_knn, bow_train, y_train, bow_test, y_test, cv=cv, message='\\nKNN BOW')\n",
        "    fine_tune(KNeighborsClassifier(), param_grid_knn, tf_idf_train, y_train, tf_idf_test, y_test, cv=cv, message='\\nKNN TF-IDF')\n",
        "    fine_tune(KNeighborsClassifier(), param_grid_knn, emb_train, y_train, emb_test, y_test, cv=cv, message='\\nKNN EMBEDDINGS')"
      ],
      "metadata": {
        "id": "oYPeeYsP_8Os"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "train(DATASETS + 'train.pkl', 0.05)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "YtEzuBx4QnaD",
        "outputId": "910e9ce3-f9a2-4e1f-d077-6d981dd2244c"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "\n",
            "SVM BOW took 353.91 seconds\n",
            "{'kernel': 'rbf', 'gamma': 'scale', 'C': 1.0}\n",
            "SVC()\n",
            "              precision    recall  f1-score   support\n",
            "\n",
            "         NEG       0.92      0.13      0.23       246\n",
            "         NEU       0.79      0.99      0.88      1365\n",
            "         POS       0.84      0.27      0.40       215\n",
            "\n",
            "    accuracy                           0.79      1826\n",
            "   macro avg       0.85      0.46      0.50      1826\n",
            "weighted avg       0.81      0.79      0.74      1826\n",
            "\n",
            "\n",
            "SVM TF-IDF took 379.25 seconds\n",
            "{'kernel': 'poly', 'gamma': 'scale', 'degree': 3, 'C': 1.0}\n",
            "SVC(kernel='poly')\n",
            "              precision    recall  f1-score   support\n",
            "\n",
            "         NEG       0.82      0.13      0.22       246\n",
            "         NEU       0.77      0.99      0.87      1365\n",
            "         POS       0.76      0.13      0.23       215\n",
            "\n",
            "    accuracy                           0.77      1826\n",
            "   macro avg       0.78      0.42      0.44      1826\n",
            "weighted avg       0.78      0.77      0.71      1826\n",
            "\n",
            "\n",
            "SVM EMBEDDINGS took 553.01 seconds\n",
            "{'kernel': 'rbf', 'gamma': 0.001, 'C': 200.0}\n",
            "SVC(C=200.0, gamma=0.001)\n",
            "              precision    recall  f1-score   support\n",
            "\n",
            "         NEG       0.71      0.45      0.55       246\n",
            "         NEU       0.84      0.95      0.89      1365\n",
            "         POS       0.79      0.43      0.56       215\n",
            "\n",
            "    accuracy                           0.82      1826\n",
            "   macro avg       0.78      0.61      0.67      1826\n",
            "weighted avg       0.82      0.82      0.81      1826\n",
            "\n",
            "\n",
            "RF BOW took 90.57 seconds\n",
            "{'n_estimators': 200, 'max_features': 'log2', 'max_depth': 100}\n",
            "RandomForestClassifier(max_depth=100, max_features='log2', n_estimators=200)\n",
            "              precision    recall  f1-score   support\n",
            "\n",
            "         NEG       0.84      0.15      0.26       246\n",
            "         NEU       0.79      0.98      0.88      1365\n",
            "         POS       0.78      0.29      0.42       215\n",
            "\n",
            "    accuracy                           0.79      1826\n",
            "   macro avg       0.80      0.47      0.52      1826\n",
            "weighted avg       0.79      0.79      0.74      1826\n",
            "\n",
            "\n",
            "RF TF-IDF took 223.66 seconds\n",
            "{'n_estimators': 200, 'max_features': 'auto', 'max_depth': 100}\n",
            "RandomForestClassifier(max_depth=100, n_estimators=200)\n",
            "              precision    recall  f1-score   support\n",
            "\n",
            "         NEG       0.79      0.24      0.37       246\n",
            "         NEU       0.81      0.98      0.89      1365\n",
            "         POS       0.77      0.40      0.53       215\n",
            "\n",
            "    accuracy                           0.81      1826\n",
            "   macro avg       0.79      0.54      0.60      1826\n",
            "weighted avg       0.81      0.81      0.78      1826\n",
            "\n",
            "\n",
            "RF EMBEDDINGS took 1827.97 seconds\n",
            "{'n_estimators': 200, 'max_features': 'auto', 'max_depth': 40}\n",
            "RandomForestClassifier(max_depth=40, n_estimators=200)\n",
            "              precision    recall  f1-score   support\n",
            "\n",
            "         NEG       0.83      0.16      0.27       246\n",
            "         NEU       0.78      0.99      0.87      1365\n",
            "         POS       0.76      0.13      0.23       215\n",
            "\n",
            "    accuracy                           0.78      1826\n",
            "   macro avg       0.79      0.43      0.46      1826\n",
            "weighted avg       0.78      0.78      0.71      1826\n",
            "\n",
            "\n",
            "KNN BOW took 20.95 seconds\n",
            "{'weights': 'distance', 'p': 1, 'n_neighbors': 10}\n",
            "KNeighborsClassifier(n_neighbors=10, p=1, weights='distance')\n",
            "              precision    recall  f1-score   support\n",
            "\n",
            "         NEG       0.77      0.07      0.13       246\n",
            "         NEU       0.76      0.99      0.86      1365\n",
            "         POS       0.75      0.06      0.10       215\n",
            "\n",
            "    accuracy                           0.76      1826\n",
            "   macro avg       0.76      0.37      0.36      1826\n",
            "weighted avg       0.76      0.76      0.67      1826\n",
            "\n",
            "\n",
            "KNN TF-IDF took 22.83 seconds\n",
            "{'weights': 'distance', 'p': 2, 'n_neighbors': 10}\n",
            "KNeighborsClassifier(n_neighbors=10, weights='distance')\n",
            "              precision    recall  f1-score   support\n",
            "\n",
            "         NEG       0.78      0.07      0.13       246\n",
            "         NEU       0.76      0.99      0.86      1365\n",
            "         POS       0.72      0.08      0.15       215\n",
            "\n",
            "    accuracy                           0.76      1826\n",
            "   macro avg       0.75      0.38      0.38      1826\n",
            "weighted avg       0.76      0.76      0.68      1826\n",
            "\n",
            "\n",
            "KNN EMBEDDINGS took 62.65 seconds\n",
            "{'weights': 'distance', 'p': 1, 'n_neighbors': 20}\n",
            "KNeighborsClassifier(n_neighbors=20, p=1, weights='distance')\n",
            "              precision    recall  f1-score   support\n",
            "\n",
            "         NEG       0.61      0.24      0.35       246\n",
            "         NEU       0.80      0.97      0.87      1365\n",
            "         POS       0.79      0.27      0.40       215\n",
            "\n",
            "    accuracy                           0.79      1826\n",
            "   macro avg       0.73      0.49      0.54      1826\n",
            "weighted avg       0.77      0.79      0.75      1826\n",
            "\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "We notice that there for different methods of text vectorizing the parameters that yield the best results differ by a lot \n",
        "\n",
        "This is not wrong, since we essentially have different input each time and we cannot expect the same parameters to work the same despite that the tweets given are the same\n",
        "\n",
        "\n",
        "We also notice that they differ a **lot** from the (almost) default we tried on Q4, which is also logical to happen \n",
        "\n",
        "We should note that although we did cross validation to meassure the performance of each parameter combination and it is indeed a good method to evaluate models when the data set is small, the parameters we found are the best for the given data set (a small fraction of the complete one) and they may differ from the best ones in general. \n",
        "\n",
        "The next step is to train the model that yields the best results on the complete training set and meassure its performance on the test set"
      ],
      "metadata": {
        "id": "nQKzoHB7kCac"
      }
    }
  ],
  "metadata": {
    "colab": {
      "provenance": [],
      "toc_visible": true
    },
    "kernelspec": {
      "display_name": "Python 3",
      "name": "python3"
    },
    "language_info": {
      "name": "python"
    }
  },
  "nbformat": 4,
  "nbformat_minor": 0
}