---
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