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Machine Learning Techniques on CrisisLexT26 dataset

This repository consists of programs which apply different Machine Learning Techniques to perform Text Classification and Topic Modelling on the CrisisLexT26 dataset. The dataset consists of tweets related to 26 disasters, which are labelled based on their informativeness. Text Classification techniques are used to predict which disaster each tweet is talking about. Topic Modelling is used to find the words that belong to the top 5 topics from the collection of tweets.

This document lists the prerequisites, execution steps and outlines the steps involved in processing the dataset and the algorithms used.

Prerequisites

The following packages were used with Python 3.5:

  • gensim
  • langid
  • tensorflow
  • keras
  • pandas
  • scikit-learn
  • xgboost

Execution

The programs can be executed as:

$ python3 topicModelling.py
$ python3 scikitClassifiers.py
$ python3 neuralNetworks.py

When the -r or --reprocessDataset option is specified, the code reads, processes and saves the processed dataset to resources/. Else the code will search for the preprocessed dataset and use it. This is to avoid the time spent on processing the dataset, instead of training the models, on subsequent executions. Hence, -r must be specified when executing the program for the first time.

For Neural Networks, the type of network (lstm, gru, mlp) can be specified as argument. If not specified, mlp is used by default.

The -p or --printMetrics option can be used to print the Classification Reports and Confusion Matrices for the models.

The above information can also be obtained by specifying the -h or --help option.

The trained models are automatically saved to models/

CrisisLexT26 dataset

The CrisisLexT26 dataset consists of the following features: Tweet ID, Tweet Text, Information Source, Information Type, Informativeness. The Tweet Text and the Informativeness are the ones we consider for applying ML. The dataset consists of tweets which can be of different languages, can be retweets and can have URL links, usernames and special characters(emojis). The tweets that are marked Not related or Not applicable do not talk about disasters and are hence labelled as Off-Topic. The tweets that are marked as Related and Informative and Related but not Informative are both labelled with the disaster's name.

Preparation of Dataset

We need the dataset to be in an appropriate form for training the Machine Learning models. The dataset is cleaned by removing tweets that are not in English, removing stop words and punctuation, removing URLs and usernames, etc., Stemming and Lemmatization are also performed. langid is used for identifying the language of the tweets and the tweets that are not in English are removed. For Text Classification, the tweets are split into unigrams and bigrams (n-grams with n=1, 2) and then converted into Count Vectors and TF-IDF Vectors. For Topic Modelling, the tweets related to each disaster are converted into Bag of Words.

Topic Modelling

For Topic Modelling, we use Latent Dirichlet Allocation (LDA) and Latent Semantic Analysis (LSA), which are provided by gensim. The models are trained on the Bag of Words and the words in the Top 5 topics are found. The implementation can be found in topicModelling.py

Text Classification

The following models are used for classifying the tweets: Logistic Regression, Naive Bayes, Random Forests, Gradient Boosting and Neural Networks. Neural Networks are implemented using keras, Gradient Boosting using XGboost and the remaining ones using scikit-learn. The Scikit Classifiers are trained on the Count Vectors and TF-IDF Vectors independently in order to compare their performance. Logistic Regression with count vectors seems to give the highest accuracy. In case of Neural Networks, the tweets are converted into Word Embeddings and then fed to Long Short-Term Memory (LSTM), Gated Recurrent Unit (GRU) or Multi-Layer Perceptron (MLP) Models, whichever is specified.

Performance Comparison of Classifiers

Model Validation Accuracy(%)
Logistic Regression, Count Vectors 93.18
Logistic Regression, TF-IDF Vectors 89.26
Naive Bayes, Count Vectors 90.26
Naive Bayes, TF-IDF Vectors 77.97
Random Forests, Count Vectors 91.81
Random Forests, TF-IDF Vectors 91.75
Gradient Boosting, Count Vectors 92.29
Gradient Boosting, TF-IDF Vectors 91.96
LSTM, Word Embeddings 91.49
GRU, Word Embeddings 91.86
MLP, Word Embeddings 90.84

References

  • A. Olteanu, C. Castillo, F. Diaz, S. Vieweg. (2014). CrisisLex: A Lexicon for Collecting and Filtering Microblogged Communications in Crises. In Proceedings of the AAAI Conference on Weblogs and Social Media (ICWSM'14). AAAI Press, Ann Arbor, MI, USA.