Comparaison Between The Implementation Of Emotion Detection From Twitter Tweets Using Svm And Lstm

Résumé: This report compares two di?erent Machine Learning (ML) methods used to classify five types of emotions from a twitter tweets dataset. The first approach is a classical method in Natural Language Processing (NLP), Support Vector Machine (SVM); The text data is cleaned, tokenized, and stemmed to derive fea-ture vectors using two di?erent feature extraction methods, namely Bag of Words (BoW) and Term Frequency-Inverse Document Frequency (TF-IDF). The resulting feature ma-trix is fed to a non-linear SVM classifier. Conversely, the second approach is more recent; this method uses word embedding and Long Short Term Memory (LSTM) neural network. First, we convert words of similar meaning into similar feature vectors. Then, the result-ing features are fed sequentially into the LSTM. Although it has been proved in the past that the SVM is the most robust model in classifica-tion problems, it is not the case for text classification. LSTM showed a better performance compared to the SVM; between 85% and 87% for LSTM and around 82% for the SVM.

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