reddit-sentiment-classifier

An NLP + LSTM based sentiment classifier trained on Reddit comments to predict positive, negative, or neutral sentiment.

This project demonstrates end-to-end workflow:

• Collecting data from Reddit API
• Cleaning and preprocessing text
• Embedding using gensim model
• Training an LSTM deep learning model
• Evaluating model performance
• Visualizing sentiment distribution and insights

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📂 Project Structure

├── data/                # Datasets (Reddit comments, posts)
├── notebooks/           # Jupyter notebooks (EDA + modeling)
│   └── Sentiment_Analysis.ipynb
├── plots/               # Saved plots/visualizations
├── requirements.txt     # Project dependencies
├── README.md            # Project documentation

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⚙️ Installation & Setup

1. Clone the repository:

git clone https://github.com/princerg/reddit-sentiment-classifier.git
cd reddit-sentiment-classifier

2. Install dependencies:

pip install -r requirements.txt

3. If collecting fresh data, configure your Reddit API:

   • Go to Reddit Apps
   • Create an application → get client_id, client_secret, and user_agent
   • Update them in the notebook before running

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📊 Dataset

• reddit_sentimnt_vader.csv → Cleaned comments with sentiment labels
• reddit_posts.csv → Metadata about posts
• comments.csv → Filterd data from reddit_posts.csv

Data was collected using the PRAW (Python Reddit API Wrapper) library.

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🧠 Model

• Preprocessing: tokenization, stopword removal, padding
• Embedding layer with FastText model (genism library)
• LSTM layers for sequential modeling
• Softmax classifier for 3 sentiment classes

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📈 Results & Insights

📌 Model Performance

Metric	Score
Accuracy	64%
Precision	0.65
Recall	0.61
F1-score	0.62

• Model achieves 64% accuracy.
• Positive comments dominate in subreddits focused on brand samsung.
• Negative comments are less likely predicted because of data imbalance.
• Visualization of sentiment distribution:

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🎯 Example Predictions

Comment	Predicted Sentiment
"The ride was so awesome!"	Positive
"this product is bad"	Negative
"I don't know what it eat toninght"	Neutral

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🚀 Future Improvements

• Integrate real-time brand sentiment tracking using Reddit API (streaming comments as they are posted).
• Build a competitor analysis dashboard to compare sentiment across multiple brands or companies.
• Explore transformer-based models (BERT) for higher accuracy.
• Deploy as an interactive Streamlit app with live charts.

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📝 License

• This project is licensed under the MIT License.