Sentiment Analysis for Customer Reviews

Overview

This project involves performing sentiment analysis on customer reviews for British Airways. The goal is to understand the sentiments expressed by customers in their feedback and categorize them into positive, negative, or neutral sentiments. This analysis helps in gaining insights into customer satisfaction and identifying areas for improvement.

Features

• Data Collection: Extracting customer reviews from various sources.
• Preprocessing: Cleaning and preparing the data for analysis.
• Sentiment Analysis: Using machine learning models to classify the sentiments of the reviews.
• Visualization: Displaying the results in an easy-to-understand format.

Installation

To get started with this project, follow these steps:

1. Clone the repository:

   git clone https://github.com/yourusername/sentiment-analysis-british-airways.git
   cd sentiment-analysis-british-airways

2. Create a virtual environment and activate it:

   python -m venv venv
   source venv/bin/activate  # On Windows, use `venv\Scripts\activate`

3. Install the required packages:

   pip install -r requirements.txt

Usage

Follow these steps to run the sentiment analysis:

1. Prepare the Data: Place the customer reviews data in the data/ directory. Ensure the data is in a CSV file with a column named review.

2. Run the Preprocessing Script:

   python preprocess.py

3. Perform Sentiment Analysis:

   python sentiment_analysis.py

4. View Results: The results will be saved in the output/ directory. You can visualize the sentiments using the provided Jupyter notebook:

   jupyter notebook visualize_results.ipynb

Project Structure

• data/: Directory to store the customer reviews data.
• preprocess.py: Script for data cleaning and preprocessing.
• sentiment_analysis.py: Script for performing sentiment analysis.
• output/: Directory to store the analysis results.
• visualize_results.ipynb: Jupyter notebook for visualizing the results.
• requirements.txt: List of required Python packages.

Contributing

Contributions are welcome! If you have any suggestions or improvements, please submit a pull request or open an issue.

License

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

Contact

For any questions or inquiries, please contact [your email address].