| language: en | |
| license: mit | |
| library_name: sklearn | |
| tags: | |
| - text-classification | |
| - sentiment-analysis | |
| - code-review | |
| - sklearn | |
| pipeline_tag: text-classification | |
| # Code Review Sentiment Classifier | |
| A lightweight sklearn-based classifier for code review comments. Classifies review feedback as positive, neutral, or negative. | |
| ## Model Details | |
| - **Type:** TF-IDF + Logistic Regression pipeline | |
| - **Task:** 3-class text classification | |
| - **Framework:** scikit-learn | |
| - **Labels:** negative (0), neutral (1), positive (2) | |
| ## Usage | |
| ```python | |
| import pickle | |
| with open("model.pkl", "rb") as f: | |
| model = pickle.load(f) | |
| review = "Great implementation, clean code!" | |
| label = model.predict([review])[0] # 0=negative, 1=neutral, 2=positive | |
| proba = model.predict_proba([review])[0] | |
| ``` | |
| ## Training Data | |
| 30 code review comments (10 per class) covering: | |
| - **Positive:** Praise, LGTM, good patterns | |
| - **Neutral:** Suggestions, minor nits, questions | |
| - **Negative:** Bugs, security issues, performance problems | |
| ## Limitations | |
| - Small training set | |
| - English only | |
| - Focused on software engineering domain | |
| ## License | |
| MIT | |