oc-p07-sentiment-api/notebooks

Notebooks

This folder contains the Jupyter notebooks used for model development and analyses in the project.

Contents

• Exploratory Data Analysis
• Classical Models (logistic regression, SVM, etc.)
• Advanced Models (LSTM with GloVe embeddings)
• BERT Model

Note on requirements files

The *requirements.txt files were generated from a Google Colab environment. This environment includes many pre-installed libraries, not all necessary for the project.

Manual cleanup or specific generation of requirements files will be needed to slim down these files before production use or deployment.

---

Model Performance Summary

Classical Models (trained on 200,000 tweets)

Model	Precision	Training Time
Random Forest + BoW	0.7798	~5 hours
Logistic Regression + TF-IDF	0.7796	<2 minutes

Advanced Models based on word-embeddings (trained on 200,000 tweets, GPU accelerated on Google Colab)

Model	Precision	Training Time
Word2Vec + LSTM	0.797	57 seconds
GloVe (300d) + LSTM	0.792	47 seconds

State-of-the-art Model (trained on 100,000 tweets due to RAM limits)

Model	Precision	Training Time
BERT	0.882	1.3 minutes

---

This summary highlights the trade-offs between model precision and training time. While the classical models are faster to train, advanced models leveraging embeddings and LSTM yield better precision, with BERT achieving the highest precision despite being trained on a smaller dataset.

The precision offset between BERT and word embeddings models may mainly be due to the BERT bidirectionnal context abilities.