Cortexelus / nlp-text-emotion

Multi-class sentiment analysis lstm, finetuned bert

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