📊 Sentiment Analysis Project

This project is a Sentiment Analysis system built using Python. It processes and analyzes customer reviews to determine their sentiment (Positive/Negative) while providing visual insights and machine learning predictions.

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✨ Features

• Data Preprocessing

  • Clean text data using NLP techniques like lemmatization and stopword removal.
  • Generate sentiment labels based on review scores.

• Data Visualization

  • Sentiment distribution analysis.
  • Generate word clouds of frequent terms.
  • Sentiment trends over time (if the dataset includes a Date column).

• Machine Learning Model

  • Train a Logistic Regression model to classify sentiment.
  • Evaluate with confusion matrices, classification reports, and cross-validation scores.

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📂 File Structure

Sentiment-Analysis-Project
├── assets/                      # Folder for images used in README  
│   ├── prprocessing.png  
│   ├── visualization.png  
│   ├── result.png  
├── data/  
│   ├── Reviews.csv                # Input dataset  
│   ├── preprocessed_reviews.csv   # Preprocessed dataset  
├── src/  
│   ├── preprocessing.py           # Script for data preprocessing  
│   ├── visualization.py           # Script for data visualization  
│   ├── model.py                   # Script for machine learning model  
├── README.md                      # Project documentation  

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🛠️ Requirements

Install all necessary dependencies using:

pip install -r requirements.txt

Key Dependencies

• pandas
• nltk
• matplotlib
• seaborn
• wordcloud
• scikit-learn

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🚀 Usage

1️⃣ Data Preprocessing Clean and preprocess the dataset using:

python src/preprocessing.py

2️⃣ Data Visualization Generate visual insights using:

python src/visualization.py

3️⃣ Model Training Train and evaluate the sentiment classification model using:

python src/model.py

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📈 Results Sentiment Over Time Trends over time based on the Date column:

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📊 Dataset

Ensure the dataset (Reviews.csv) is in the data/ directory.

Minimum required columns:

1. Text - Customer reviews.
2. Score - Ratings for the reviews.

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🤝 Contributing

I welcome contributions! To contribute:

1. Fork the repository.

2. Create a new branch:

   git checkout -b feature-branch
   

3. Commit your changes:

   git commit -m "Add a new feature"
   

4. Push to your branch:

   git push origin feature-branch
   

5. Submit a Pull Request.

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📜 License This project is licensed under the MIT License. See the LICENSE file for details.

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👤 Author : Deepti Gupta

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