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+# Emotion Classification in Short Messages
+
+Multi-class sentiment analysis problem to classify text into five emotions 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's keras).
+
+# Datasets
+
+## Datasets overview
+
+**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](http://yanran.li/dailydialog.html), [emotion-stimulus](http://www.site.uottawa.ca/~diana/resources/emotion_stimulus_data), [isear](http://www.affective-sciences.org/index.php/download_file/view/395/296/)
+
+
+## Combined dataset
+
+Dataset was combined from dailydialog, isear, and emotion-stimulus to create a balanced dataset with 6 labels: joy, sad, anger, fear, disgust, surprise 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](https://fasttext.cc/docs/en/english-vectors.html))
+* Deep Network: LSTM and biLSTM
+
+| Approach            | F1-Score |
+| :------------------ | :------: |
+| LSTM + w2v_wiki     | 0.7395   |
+| biLSTM + w2v_wiki   | 0.7414   |
+
+### Transfer learning with BERT
+Fine-tuning BERT for text classification
+
+| Approach            | F1-Score |
+| :------------------ | :------: |
+| fine-tuned BERT     | 0.8320   |