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Emotion Classification in Short Messages

Multi-class sentiment analysis problem to classify texts into five emotion categories: joy, sadness, anger, fear, neutral. A fun weekend project to go through different text classification techniques. This includes dataset preparation, traditional machine learning with scikit-learn, LSTM neural networks and transfer learning using BERT (tensorflow keras).

Datasets

Summary Table

Dataset Year Content Size Emotion categories Balanced
dailydialog 2017 dialogues 102k sentences neutral, joy, surprise, sadness, anger, disgust, fear No
emotion-stimulus 2015 dialogues 2.5k sentences sadness, joy, anger, fear, surprise, disgust No
isear 1990 emotional situations 7.5k sentences joy, fear, anger, sadness, disgust, shame, guilt Yes

links: dailydialog, emotion-stimulus, isear

Combined dataset

Dataset was combined from dailydialog, isear, and emotion-stimulus to create a balanced dataset with 5 labels: joy, sad, anger, fear, and neutral. The texts mainly consist of short messages and dialog utterances.

Experiments

Traditional Machine Learning:

  • Data preprocessing: noise and punctuation removal, tokenization, stemming
  • Text Representation: TF-IDF
  • Classifiers: Naive Bayes, Random Forrest, Logistic Regrassion, SVM
Approach F1-Score
Naive Bayes 0.6702
Random Forrest 0.6372
Logistic Regression 0.6935
SVM 0.7271

Neural Networks

  • Data preprocessing: noise and punctuation removal, tokenization
  • Word Embeddings: pretrained 300 dimensional word2vec (link)
  • Deep Network: LSTM, biLSTM, CNN
Approach F1-Score
LSTM + w2v_wiki 0.7395
biLSTM + w2v_wiki 0.7414
CNN + w2v_wiki 0.7580

Transfer learning with BERT

Finetuning BERT for text classification

Approach F1-Score
finetuned BERT 0.8320

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Multi-class sentiment analysis lstm, finetuned bert

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