project demonstrates the process of building a sentiment analysis model for movie reviews using NLP techniques. It covers data preprocessing, feature extraction, model selection, hyperparameter tuning, and model evaluation. The goal is to accurately classify movie reviews as positive or negative based on their textual content,
active 2023-10-03 → 2024-06-07 (UTC)
Activity over time
Daily event counts in the loaded window
Line chart, 249 days from 2023-10-03 to 2024-06-07. Pushes: 5 total, peak 5 in a day. Pull requests: 0 total, peak 0 in a day. Issues: 0 total, peak 0 in a day. Comments: 0 total, peak 0 in a day. Stars: 3 total, peak 1 in a day.
- Pushes
- Pull requests
- Issues
- Comments
- Stars
Top contributors
Pushes, PRs, issues, reviews and comments — stars and forks excluded, so this is contribution rather than popularity
| Contributor | Contributions | Pushes | PRs | Comments |
|---|---|---|---|---|
| Malbasahi | 5 | 5 | 0 | 0 |
Recent activity
Latest issues, pull requests and releases
No issue or PR events — this repo's activity is pushes only.
Totals cover only the window loaded into ClickHouse and count events, not GitHub's lifetime totals — 3 stars here means stars gained during the window, not the repo's star count.