anashas/AI4D-iCompass-Social-Media-Sentiment-Analysis-for-Tunisian-Arabizi

### AI4D-iCompass-Social-Media-Sentiment-Analysis-for-Tunisian-Arabizi Competition website - This is an NLP project about Tunisian Arabizi sentiment analysis - The goal is to classify the sentiments into 3 categories ```positive (1), negative (-1), and neutral (0)``` - Tunisian Arabizi contains different languages: Arabizi, French, and English - Established a baseline with Naive Bayes which gave a good accuracy value, mainly because the dataset was highly imbalanced - Due to the small size of the dataset, I preferred to fine-tune a bert model: experimented with different models and found that the ```bert-base-multilingual-cased``` from the ```Hugging Face``` model Hub performed better than the others. - Added a ```Conv1D``` layer on top of the bert model, followed by a ```GlobalAveragePooling1D``` - Used ```Adam``` optimizer with 2e-5 as a learning rate and fine-tuned the model for 4 epochs. - Tried different strategies to improve model accuracy such as layer-wise learning rate but the performance did not improve much - Final submission on the Leaderboard ```0.8224``` - ```notebook/train.ipynb``` contains the training notebook