Cyberpunk reviews.ipynb

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   "source": [
    "import pandas as pd\n",
    "import re\n",
    "from bs4 import BeautifulSoup\n",
    "import unicodedata\n",
    "import numpy as np\n",
    "\n",
    "# NLTK imports\n",
    "import nltk\n",
    "from nltk.corpus import stopwords\n",
    "from nltk.tokenize import sent_tokenize \n",
    "\n",
    "# Gensim imports\n",
    "import gensim \n",
    "from gensim.models import Word2Vec\n",
    "\n",
    "# Spacy imports\n",
    "import spacy\n",
    "nlp = spacy.load('en', parse = False, tag=False, entity=False)\n",
    "\n",
    "import contractions\n",
    "\n",
    "# Plotly imports\n",
    "import plotly.offline as py\n",
    "import plotly.graph_objs as go\n",
    "import plotly.tools as tls\n",
    "# Set notebook mode to work in offline\n",
    "py.init_notebook_mode(connected=True)\n",
    "import cufflinks as cf\n",
    "cf.go_offline()\n",
    "cf.set_config_file(offline=False, world_readable=True)\n",
    "\n",
    "# Image\n",
    "import cv2\n",
    "import imutils\n",
    "\n",
    "# Wordcloud\n",
    "from matplotlib import pyplot as plt\n",
    "from wordcloud import WordCloud, STOPWORDS"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Import data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {},
   "outputs": [
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>date</th>\n",
       "      <th>text</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>2021-01-08 14:49:38</td>\n",
       "      <td>Funny man go shoot</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2021-01-08 14:49:08</td>\n",
       "      <td>Lots of glitches but at least for me they take...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2021-01-08 14:49:59</td>\n",
       "      <td>would only reccomend if they are planning a re...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>2021-01-08 14:47:00</td>\n",
       "      <td>Despite all of the negative buzz, I have had a...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>2021-01-08 14:42:34</td>\n",
       "      <td>It's a bugged and technically very broken expe...</td>\n",
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      "text/plain": [
       "                 date                                               text\n",
       "0 2021-01-08 14:49:38                                 Funny man go shoot\n",
       "1 2021-01-08 14:49:08  Lots of glitches but at least for me they take...\n",
       "2 2021-01-08 14:49:59  would only reccomend if they are planning a re...\n",
       "3 2021-01-08 14:47:00  Despite all of the negative buzz, I have had a...\n",
       "4 2021-01-08 14:42:34  It's a bugged and technically very broken expe..."
      ]
     },
     "execution_count": 36,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df = pd.read_csv(\"data/reviews.csv\", header=None)\n",
    "df.columns = [\"date\", \"text\"]\n",
    "df['date'] = pd.to_datetime(df['date'],unit='s')\n",
    "df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(7096, 2)"
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     "execution_count": 37,
     "metadata": {},
     "output_type": "execute_result"
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   ],
   "source": [
    "df.shape"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Clean data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "metadata": {},
   "outputs": [],
   "source": [
    "STOPWORDS = set(stopwords.words('english')+[\"game\", \"really\", \"would\", \"one\", \"much\", \n",
    "                                            \"many\", \"could\", \"way\", \"also\", \"feel\", \"like\", \"think\", 'get', 'play', \n",
    "                                            'still', 'even', 'say', 'make'])\n",
    "\n",
    "# Cleaning Text - strip HTML\n",
    "def strip_html_tags(text):\n",
    "    soup = BeautifulSoup(text)\n",
    "    stripped_text = soup.get_text()\n",
    "    return stripped_text\n",
    "\n",
    "# Removing accented characters\n",
    "def remove_accented_chars(text):\n",
    "    text = unicodedata.normalize('NFKD', text).encode('ascii', 'ignore').decode('utf-8', 'ignore')\n",
    "    return text\n",
    "\n",
    "# Removing Special Characters\n",
    "def remove_special_characters(text):\n",
    "    text = re.sub('[^a-z ]', '', str(text))\n",
    "    return text\n",
    "\n",
    "# Lemmatize text\n",
    "def lemmatize_text(text):\n",
    "    text = nlp(text) #tokenize\n",
    "    text = [word.lemma_ if word.lemma_ != '-PRON-' else word.text for word in text]\n",
    "    return text\n",
    "\n",
    "def clean_text(text):\n",
    "    \"\"\"\n",
    "        text: a string\n",
    "        \n",
    "        return: modified initial string\n",
    "    \"\"\"\n",
    "    text = str(text).lower() # lowercase text\n",
    "    text = strip_html_tags(text) # strip html tags\n",
    "    text = remove_accented_chars(text) # remove accented chars\n",
    "    text = contractions.fix(text) # expand contractions\n",
    "    text = nltk.word_tokenize(text) # tokenize \n",
    "    text = [remove_special_characters(x) for x in text if remove_special_characters(x)] # remove special characters \n",
    "    text = lemmatize_text(\" \".join(text)) # lemmatize \n",
    "    text = ' '.join(word for word in text if word not in STOPWORDS and len(word) > 2) # remove stopwords and join the tokens\n",
    "    return text\n",
    "    \n",
    "df['text_clean'] = df['text'].apply(clean_text)"
   ]
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  {
   "cell_type": "code",
   "execution_count": 39,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "\"The core of the game is honestly fun, but the glitches make the experience inconsistent as it constantly breaks your immersion. If you've waited this long already to purchase (1/8/2021), I'd suggest just holding out for a few more months until the kinks get ironed out for the best experience. \\r\\n\\r\\nThat being said; I've nearly 100%'d the game and absolutely loved it. Glitches were frustrating sometimes, causing quests to not finish, etc, but the core gameplay mechanics were all fun and enjoyable. Gunplay could use some work, but chaining together quickhacks with melee/stealth kills and occasional headshots from absurd distances are fun and feel rewarding when accomplished without errors. The game is (obviously) heavy on dialogue due to the nature of it being an open-world RPG, but you'll find yourself easily drawn into the stories of the inhabitants of Night City.  Wait for patches and then enjoy what will ultimately be a great cyberpunk experience 6 months from now. Looking forward to\""
      ]
     },
     "execution_count": 39,
     "metadata": {},
     "output_type": "execute_result"
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   ],
   "source": [
    "df.text[5]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'core honestly fun glitch experience inconsistent constantly break immersion wait long already purchase suggest hold month kink iron good experience nearly absolutely love glitche frustrating sometimes cause quest finish etc core gameplay mechanic fun enjoyable gunplay use work chain together quickhack meleestealth kill occasional headshot absurd distance fun rewarding accomplish without error obviously heavy dialogue due nature openworld rpg find easily draw story inhabitant night city wait patch enjoy ultimately great cyberpunk experience month look forward'"
      ]
     },
     "execution_count": 40,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.text_clean[5]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "metadata": {},
   "outputs": [],
   "source": [
    "df.to_csv(\"data/clean_reviews.csv\", index=False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "      <th>date</th>\n",
       "      <th>text</th>\n",
       "      <th>text_clean</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>2021-01-08 14:49:38</td>\n",
       "      <td>Funny man go shoot</td>\n",
       "      <td>funny man shoot</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2021-01-08 14:49:08</td>\n",
       "      <td>Lots of glitches but at least for me they take...</td>\n",
       "      <td>lot glitch least take nothing away hope add cu...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2021-01-08 14:49:59</td>\n",
       "      <td>would only reccomend if they are planning a re...</td>\n",
       "      <td>reccomend plan redux june include cut content ...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>2021-01-08 14:47:00</td>\n",
       "      <td>Despite all of the negative buzz, I have had a...</td>\n",
       "      <td>despite negative buzz positive experience litt...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>2021-01-08 14:42:34</td>\n",
       "      <td>It's a bugged and technically very broken expe...</td>\n",
       "      <td>bugged technically broken experience run gtx s...</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
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      "text/plain": [
       "                  date                                               text  \\\n",
       "0  2021-01-08 14:49:38                                 Funny man go shoot   \n",
       "1  2021-01-08 14:49:08  Lots of glitches but at least for me they take...   \n",
       "2  2021-01-08 14:49:59  would only reccomend if they are planning a re...   \n",
       "3  2021-01-08 14:47:00  Despite all of the negative buzz, I have had a...   \n",
       "4  2021-01-08 14:42:34  It's a bugged and technically very broken expe...   \n",
       "\n",
       "                                          text_clean  \n",
       "0                                    funny man shoot  \n",
       "1  lot glitch least take nothing away hope add cu...  \n",
       "2  reccomend plan redux june include cut content ...  \n",
       "3  despite negative buzz positive experience litt...  \n",
       "4  bugged technically broken experience run gtx s...  "
      ]
     },
     "execution_count": 42,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df = pd.read_csv(\"data/clean_reviews.csv\")\n",
    "df.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Explore"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Word count distribution"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "metadata": {},
   "outputs": [
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     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Number of descriptions: 7096 \n",
      "Average word count 33.11640360766629 \n",
      "Minimum word count 1 \n",
      "Maximum word count 835\n"
     ]
    },
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   "source": [
    "df['word_count'] = df['text_clean'].apply(lambda x: len(str(x).split()))\n",
    "desc_lengths = list(df['word_count'])\n",
    "print(\"Number of descriptions:\",len(desc_lengths),\n",
    "      \"\\nAverage word count\", np.average(desc_lengths),\n",
    "      \"\\nMinimum word count\", min(desc_lengths),\n",
    "      \"\\nMaximum word count\", max(desc_lengths))\n",
    "\n",
    "df['word_count'].iplot(\n",
    "    kind='hist',\n",
    "    bins = 50,\n",
    "    linecolor='black',\n",
    "    xTitle='word count',\n",
    "    yTitle='count',\n",
    "    title='Word Count Distribution')"
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  {
   "cell_type": "code",
   "execution_count": 44,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Delete the reviews with 30 words or less\n",
    "df = df[(df.word_count > 30)].copy()"
   ]
  },
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   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Most common words"
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  {
   "cell_type": "code",
   "execution_count": 45,
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     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "all_words = df['text_clean'].str.split(expand=True).unstack().value_counts()\n",
    "data = [go.Bar(\n",
    "            x = all_words.index.values[0:50],\n",
    "            y = all_words.values[0:50],\n",
    "            marker= dict(colorscale='Jet',\n",
    "                         color = all_words.values[0:100]\n",
    "                        ),\n",
    "            text='Word counts'\n",
    "    )]\n",
    "\n",
    "layout = go.Layout(\n",
    "    title='Top 50 Word frequencies'\n",
    ")\n",
    "\n",
    "fig = go.Figure(data=data, layout=layout)\n",
    "\n",
    "py.iplot(fig, filename='basic-bar')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "metadata": {},
   "outputs": [],
   "source": [
    "def load_transparent_image(path):\n",
    "    \"\"\"\n",
    "    The transparency will be loaded as white so it's ready for the wordcloud\n",
    "    \"\"\"\n",
    "    # load image with alpha channel\n",
    "    image = cv2.imread(path, cv2.IMREAD_UNCHANGED)    \n",
    "\n",
    "    # make mask of where the transparent bits are\n",
    "    trans_mask = image[:,:,3] == 0\n",
    "\n",
    "    # replace areas of transparency with white and not transparent\n",
    "    image[trans_mask] = [255, 255, 255, 255]\n",
    "\n",
    "    # new image without alpha channel\n",
    "    new_img = cv2.cvtColor(image, cv2.COLOR_BGRA2BGR)\n",
    "    \n",
    "    # resize it\n",
    "    new_img = imutils.resize(new_img, width=450, height=None, inter=cv2.INTER_AREA)\n",
    "\n",
    "    return new_img\n",
    "\n",
    "def print_wordcloud(text, mask, title, size=(12,8)):\n",
    "    \"\"\"\n",
    "    Print a wordcloud with a mask image\n",
    "    \"\"\"\n",
    "    plt.figure(figsize=size)\n",
    "    wc = WordCloud(background_color=\"black\", max_words=5000, \n",
    "                   mask=mask, stopwords=STOPWORDS, max_font_size= 40, random_state=0)\n",
    "    wc.generate(\" \".join(text))\n",
    "    plt.title(title, fontsize=20)\n",
    "    plt.imshow(wc.recolor( colormap= 'Pastel2' , random_state=17), alpha=0.98)\n",
    "    plt.axis('off')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 50,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1152x936 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Wordcloud\n",
    "image = load_transparent_image(\"img/cyberpunk-mask.png\")\n",
    "\n",
    "print_wordcloud(df.text_clean.values.tolist(), image, \"Cyberpunk\", (16,13))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Bigrams and Trigrams"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 51,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.feature_extraction.text import CountVectorizer\n",
    "\n",
    "def get_top_n_bigram(corpus, n, ngram):\n",
    "    vec = CountVectorizer(ngram_range=(ngram, ngram)).fit(corpus)\n",
    "    bag_of_words = vec.transform(corpus)\n",
    "    sum_words = bag_of_words.sum(axis=0) \n",
    "    words_freq = [(word, sum_words[0, idx]) for word, idx in vec.vocabulary_.items()]\n",
    "    words_freq =sorted(words_freq, key = lambda x: x[1], reverse=True)\n",
    "    return words_freq[:n]\n",
    "\n",
    "def plot_n_bigram(corpus, title, n=20, ngram=2):\n",
    "    common_words = get_top_n_bigram(corpus, n, ngram)\n",
    "\n",
    "    df3 = pd.DataFrame(common_words, columns = ['clean' , 'count'])\n",
    "    df3.groupby('clean').sum()['count'].sort_values(ascending=False).iplot(\n",
    "        kind='bar', yTitle='Count', linecolor='black', title=f'Top {n} {ngram}-grams: {title}')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 52,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "application/vnd.plotly.v1+json": {
       "config": {
        "linkText": "Export to plot.ly",
        "plotlyServerURL": "https://plot.ly",
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           "width": 1
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         },
         "name": "count",
         "orientation": "v",
         "text": "",
         "type": "bar",
         "x": [
          "side quest",
          "night city",
          "open world",
          "main story",
          "side mission",
          "main quest",
          "story line",
          "break bug",
          "projekt red",
          "bug glitch",
          "current state",
          "good story",
          "bug fix",
          "johnny silverhand",
          "story character",
          "cut content",
          "pretty good",
          "great story",
          "look forward",
          "story side",
          "life path",
          "chad chad",
          "skill tree",
          "story good",
          "lot bug"
         ],
         "y": [
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         ]
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        "paper_bgcolor": "#FFFFFF",
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            "type": "choropleth"
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            "type": "contour"
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          "contourcarpet": [
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            "colorbar": {
             "outlinewidth": 0,
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            "type": "contourcarpet"
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          "heatmap": [
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            "colorscale": [
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            "type": "heatmap"
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          ],
          "heatmapgl": [
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     "metadata": {},
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   ],
   "source": [
    "plot_n_bigram(df.text_clean, \"Reviews\", n=25, ngram=3)"
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  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Word2Vec"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 54,
   "metadata": {},
   "outputs": [],
   "source": [
    "good_token_clean = []\n",
    "# list of list of tokens [[\"this\", \"is\", \"one\", \"sentence\"], [\"another\", \"one\"],..]\n",
    "for r in df.text_clean.values.tolist():\n",
    "    good_token_clean.append(str(r).split())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 76,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(4160594, 4747175)"
      ]
     },
     "execution_count": 76,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# https://radimrehurek.com/gensim/models/word2vec.html#gensim.models.word2vec.Word2Vec\n",
    "model = Word2Vec(min_count = 3) #cbow\n",
    "model.build_vocab(good_token_clean)\n",
    "model.train(good_token_clean, total_examples = model.corpus_count, epochs = 25)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 77,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Backup the model\n",
    "model.save(\"word2vec.model\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 79,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Load the model from file\n",
    "model = Word2Vec.load(\"word2vec.model\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 133,
   "metadata": {},
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       "      <th>issue</th>\n",
       "      <td>performance</td>\n",
       "      <td>major</td>\n",
       "      <td>big</td>\n",
       "      <td>technical</td>\n",
       "      <td>fix</td>\n",
       "      <td>solve</td>\n",
       "      <td>resolve</td>\n",
       "      <td>gamebreaking</td>\n",
       "      <td>minor</td>\n",
       "      <td>glaring</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>performance</th>\n",
       "      <td>issue</td>\n",
       "      <td>optimization</td>\n",
       "      <td>performance</td>\n",
       "      <td>dlss</td>\n",
       "      <td>keanu</td>\n",
       "      <td>gtx</td>\n",
       "      <td>rtx</td>\n",
       "      <td>ram</td>\n",
       "      <td>poor</td>\n",
       "      <td>mhz</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>look</th>\n",
       "      <td>forward</td>\n",
       "      <td>past</td>\n",
       "      <td>gorgeous</td>\n",
       "      <td>visually</td>\n",
       "      <td>stunning</td>\n",
       "      <td>clown</td>\n",
       "      <td>amazing</td>\n",
       "      <td>beautiful</td>\n",
       "      <td>looking</td>\n",
       "      <td>great</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                       0             1              2           3  \\\n",
       "story               main     excellent     compelling        line   \n",
       "glitch         graphical  gamebreaking          minor  gamebreake   \n",
       "bug            graphical  gamebreaking          minor       break   \n",
       "quest               side          ncpd           main       fetch   \n",
       "character       creation   development  customization       voice   \n",
       "city               night          live       district       alive   \n",
       "gameplay            core          mehh      enjoyable    gameplay   \n",
       "fun               combat       hacking          super     gunplay   \n",
       "world               open      building           live    detailed   \n",
       "bad              driving           bad        eargasm        deaf   \n",
       "issue        performance         major            big   technical   \n",
       "performance        issue  optimization    performance        dlss   \n",
       "look             forward          past       gorgeous    visually   \n",
       "\n",
       "                       4           5          6             7             8  \\\n",
       "story            amazing     telling      short     fantastic           arc   \n",
       "glitch          breaking   encounter     glitch         lucky        visual   \n",
       "bug            encounter  gamebreake      major        plague      breaking   \n",
       "quest             marker   secondary        gig         fixer         chain   \n",
       "character    personality        meet    connect        create    believable   \n",
       "city            outskirt     badland  wasteland          huge  architecture   \n",
       "gameplay            loop       solid       hour         watch      mechanic   \n",
       "fun                melee         lot        ton      gameplay      shooting   \n",
       "world            vibrant       dense    immerse     immersive          fill   \n",
       "bad                 list       press    glitchy      audience         audio   \n",
       "issue                fix       solve    resolve  gamebreaking         minor   \n",
       "performance        keanu         gtx        rtx           ram          poor   \n",
       "look            stunning       clown    amazing     beautiful       looking   \n",
       "\n",
       "                     9  \n",
       "story           linear  \n",
       "glitch           funny  \n",
       "bug                ark  \n",
       "quest           filler  \n",
       "character    memorable  \n",
       "city           explore  \n",
       "gameplay     narrative  \n",
       "fun                fun  \n",
       "world            empty  \n",
       "bad             ending  \n",
       "issue          glaring  \n",
       "performance        mhz  \n",
       "look             great  "
      ]
     },
     "execution_count": 133,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "words_to_explore = [\"story\", \"glitch\", \"bug\", \"quest\", \"character\", \"city\", \"gameplay\", \"fun\", \n",
    "                    \"world\", \"bad\", \"issue\", \"performance\", \"look\"]\n",
    "\n",
    "what_they_say = pd.DataFrame()\n",
    "for w in words_to_explore:\n",
    "    predicted = model.predict_output_word([w], topn=10)\n",
    "    predicted = [c[0] for c in predicted]\n",
    "    what_they_say[w] = predicted\n",
    "what_they_say.T"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 120,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[('main', 0.50519806),\n",
       " ('excellent', 0.03421433),\n",
       " ('compelling', 0.030839624),\n",
       " ('line', 0.02635204),\n",
       " ('amazing', 0.019248268),\n",
       " ('telling', 0.015342619),\n",
       " ('short', 0.012731631),\n",
       " ('fantastic', 0.0099292295),\n",
       " ('arc', 0.0096724965),\n",
       " ('linear', 0.009271319)]"
      ]
     },
     "execution_count": 120,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "model.predict_output_word(['story'], topn=10)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 81,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[('graphical', 0.72210723),\n",
       " ('gamebreaking', 0.038972057),\n",
       " ('minor', 0.033700082),\n",
       " ('gamebreake', 0.013320996),\n",
       " ('breaking', 0.01314418),\n",
       " ('encounter', 0.010319149),\n",
       " ('glitch', 0.00967622),\n",
       " ('lucky', 0.008858773),\n",
       " ('visual', 0.008113448),\n",
       " ('funny', 0.00808832)]"
      ]
     },
     "execution_count": 81,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "model.predict_output_word(['glitch'], topn=10)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 82,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[('side', 0.99162745),\n",
       " ('final', 0.0046237647),\n",
       " ('ncpd', 0.000655667),\n",
       " ('fetch', 0.0002831621),\n",
       " ('complete', 0.00023197787),\n",
       " ('chain', 0.00018885774),\n",
       " ('fixer', 0.00017022585),\n",
       " ('main', 0.00011118013),\n",
       " ('marker', 0.00010949936),\n",
       " ('paste', 8.936826e-05)]"
      ]
     },
     "execution_count": 82,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "model.predict_output_word(['mission'], topn=10)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 83,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[('side', 0.9995283),\n",
       " ('ncpd', 0.00011973515),\n",
       " ('main', 9.71357e-05),\n",
       " ('fetch', 5.101552e-05),\n",
       " ('marker', 3.535235e-05),\n",
       " ('secondary', 1.5315709e-05),\n",
       " ('gig', 1.1808107e-05),\n",
       " ('fixer', 1.1240586e-05),\n",
       " ('chain', 9.823638e-06),\n",
       " ('filler', 6.3435823e-06)]"
      ]
     },
     "execution_count": 83,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "model.predict_output_word(['quest'], topn=10)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 84,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[('creation', 0.33357978),\n",
       " ('development', 0.11808425),\n",
       " ('customization', 0.05994803),\n",
       " ('voice', 0.042564854),\n",
       " ('personality', 0.041765228),\n",
       " ('meet', 0.033133786),\n",
       " ('connect', 0.0233858),\n",
       " ('create', 0.018812858),\n",
       " ('believable', 0.017721424),\n",
       " ('memorable', 0.016308568)]"
      ]
     },
     "execution_count": 84,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "model.predict_output_word(['character'], topn=10)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 85,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[('night', 0.99999994),\n",
       " ('live', 1.04461e-08),\n",
       " ('district', 9.809647e-09),\n",
       " ('alive', 5.5733893e-09),\n",
       " ('outskirt', 4.4528514e-09),\n",
       " ('badland', 3.5489227e-09),\n",
       " ('wasteland', 1.8821324e-09),\n",
       " ('huge', 1.8084021e-09),\n",
       " ('architecture', 1.6822244e-09),\n",
       " ('explore', 1.4990835e-09)]"
      ]
     },
     "execution_count": 85,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "model.predict_output_word(['city'], topn=10)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 86,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[('core', 0.11096901),\n",
       " ('mehh', 0.037070062),\n",
       " ('enjoyable', 0.0350946),\n",
       " ('gameplay', 0.034710836),\n",
       " ('loop', 0.025445573),\n",
       " ('solid', 0.019296242),\n",
       " ('hour', 0.018905954),\n",
       " ('watch', 0.014738745),\n",
       " ('mechanic', 0.014236664),\n",
       " ('narrative', 0.011962202)]"
      ]
     },
     "execution_count": 86,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "model.predict_output_word(['gameplay'], topn=10)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 87,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[('spend', 0.75988954),\n",
       " ('long', 0.026785966),\n",
       " ('waste', 0.022940079),\n",
       " ('take', 0.014557334),\n",
       " ('slow', 0.0028509202),\n",
       " ('load', 0.002603765),\n",
       " ('worth', 0.0020663817),\n",
       " ('first', 0.0020565735),\n",
       " ('sink', 0.0019849928),\n",
       " ('put', 0.0019334216)]"
      ]
     },
     "execution_count": 87,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "model.predict_output_word(['time'], topn=10)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 88,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[('graphical', 0.20071968),\n",
       " ('gamebreaking', 0.11896904),\n",
       " ('minor', 0.1002312),\n",
       " ('break', 0.0468213),\n",
       " ('encounter', 0.040398005),\n",
       " ('gamebreake', 0.03502629),\n",
       " ('major', 0.029834488),\n",
       " ('plague', 0.01972167),\n",
       " ('breaking', 0.018843006),\n",
       " ('ark', 0.012776832)]"
      ]
     },
     "execution_count": 88,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "model.predict_output_word(['bug'], topn=10)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 89,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[('combat', 0.36499366),\n",
       " ('hacking', 0.05326944),\n",
       " ('super', 0.027182702),\n",
       " ('gunplay', 0.027164059),\n",
       " ('melee', 0.02676222),\n",
       " ('lot', 0.015553213),\n",
       " ('ton', 0.011944921),\n",
       " ('gameplay', 0.011435628),\n",
       " ('shooting', 0.011040469),\n",
       " ('fun', 0.010394711)]"
      ]
     },
     "execution_count": 89,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "model.predict_output_word(['fun'], topn=10)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 90,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[('open', 0.9999964),\n",
       " ('building', 1.5930366e-06),\n",
       " ('live', 1.5500635e-07),\n",
       " ('detailed', 1.4346575e-07),\n",
       " ('vibrant', 1.1964217e-07),\n",
       " ('dense', 1.13013265e-07),\n",
       " ('immerse', 9.9138035e-08),\n",
       " ('immersive', 9.775783e-08),\n",
       " ('fill', 7.475389e-08),\n",
       " ('empty', 6.766177e-08)]"
      ]
     },
     "execution_count": 90,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "model.predict_output_word(['world'], topn=10)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 91,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[('driving', 0.30598354),\n",
       " ('bad', 0.14881206),\n",
       " ('eargasm', 0.045491584),\n",
       " ('deaf', 0.043161087),\n",
       " ('list', 0.0353267),\n",
       " ('press', 0.016260242),\n",
       " ('glitchy', 0.013072009),\n",
       " ('audience', 0.011796696),\n",
       " ('audio', 0.011662388),\n",
       " ('ending', 0.010810661)]"
      ]
     },
     "execution_count": 91,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "model.predict_output_word(['bad'], topn=10)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 92,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[('performance', 0.34685674),\n",
       " ('major', 0.2706016),\n",
       " ('big', 0.14857271),\n",
       " ('technical', 0.077452466),\n",
       " ('fix', 0.06331345),\n",
       " ('solve', 0.035506006),\n",
       " ('resolve', 0.0075558233),\n",
       " ('gamebreaking', 0.0051228264),\n",
       " ('minor', 0.0030097337),\n",
       " ('glaring', 0.002804519)]"
      ]
     },
     "execution_count": 92,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "model.predict_output_word(['issue'], topn=10)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 93,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[('highly', 0.95643365),\n",
       " ('anyone', 0.04115212),\n",
       " ('current', 0.00038926833),\n",
       " ('spec', 0.00032269015),\n",
       " ('wholeheartedly', 0.00025940235),\n",
       " ('definitely', 0.00016557651),\n",
       " ('state', 0.00015614532),\n",
       " ('sale', 6.726168e-05),\n",
       " ('buck', 4.8708775e-05),\n",
       " ('overall', 4.6838617e-05)]"
      ]
     },
     "execution_count": 93,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "model.predict_output_word(['recommend'], topn=10)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 95,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[('sale', 0.71205),\n",
       " ('wait', 0.261522),\n",
       " ('price', 0.010482874),\n",
       " ('discount', 0.0016808651),\n",
       " ('buy', 0.0013858584),\n",
       " ('pick', 0.0013582106),\n",
       " ('grab', 0.0009787406),\n",
       " ('buck', 0.0009485429),\n",
       " ('regret', 0.00072446495),\n",
       " ('worth', 0.0006159873)]"
      ]
     },
     "execution_count": 95,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "model.predict_output_word(['sale'], topn=10)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 123,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[('issue', 0.9124076),\n",
       " ('optimization', 0.025927234),\n",
       " ('performance', 0.015971797),\n",
       " ('dlss', 0.00239419),\n",
       " ('keanu', 0.0023837034),\n",
       " ('gtx', 0.0022998727),\n",
       " ('rtx', 0.001835262),\n",
       " ('ram', 0.0016648264),\n",
       " ('poor', 0.0016568814),\n",
       " ('mhz', 0.0009873766)]"
      ]
     },
     "execution_count": 123,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "model.predict_output_word(['performance'], topn=10)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 97,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[('forward', 0.9686904),\n",
       " ('past', 0.008195078),\n",
       " ('gorgeous', 0.002766104),\n",
       " ('visually', 0.0024431976),\n",
       " ('stunning', 0.0014082987),\n",
       " ('clown', 0.0012384865),\n",
       " ('amazing', 0.0008943065),\n",
       " ('beautiful', 0.00084512547),\n",
       " ('looking', 0.00082656916),\n",
       " ('great', 0.0005984208)]"
      ]
     },
     "execution_count": 97,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "model.predict_output_word(['look'], topn=10)"
   ]
  }
 ],
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