Customer Sentiment Analysis

This project aims to analyze customer reviews using various Natural Language Processing (NLP) techniques. The system utilizes Vader's Sentiment Analysis, Support Vector Classifier, and Multinomial Naive Bayes algorithms to classify and understand the sentiment expressed in the reviews. Additionally, the project includes a frontend and backend developed using Flask, a Python web framework.

Preview of the Website

Project Structure

• 📁 static/: CSS for the html pages.
• 📁 templates/: HTML pages for the frontend.
• 📄 app.py/: Contains the flask code for backend.
• 📄 Reviews.ipynb/: Notebook containing the algorithms and graphs for various models.
• 📄 Reviews.py/: Contains OOPs concepts involved in our project.
• 📄 AD.csv/: Dataset for our project.
• 📄 AD_cleaned.csv/: Dataset after tokenization and lemmatization.
• 📄 sentiment_analysis_results.csv/: Dataset with results.

Getting Started

To get started with SoulHouse, follow these steps:

1. Clone the repository: git clone https://github.com/JainSneha6/CustomerReviewAnalysis.git

2. Navigate to the project directory: cd CustomerReviewAnalysis

3. Create a virtual environment:

   • python3 -m venv venv

4. Activate the virtual environment:

   • On Windows: venv\Scripts\activate
   • On macOS and Linux: source venv/bin/activate

5. Install dependencies:

   • Flask
   • nltk
   • scikit-learn
   • vaderSentiment
   • pandas
   • numpy
   • matplotlib

6. Run the backend server (runs on port 5000 by default):

   • python app.py

7. Open your web browser

Contributing

Contributions to this project are welcome! If you have suggestions for improvements or would like to contribute new features or analyses, feel free to submit a pull request

Contact

For any questions or feedback, feel free to reach out:

• Sneha Jain - GitHub | LinkedIn
• Siddhartha Chakrabarty - GitHub | LinkedIn
• Project Repository

Uncover insights in customer reviews effortlessly! 📊