Bank Review Sentiment Analysis and Service Search Platform

Overview

This project is a Natural Language Processing (NLP) and Text Mining platform designed to analyze customer reviews for various banks. Using a machine learning model, the platform extracts sentiments (positive, negative, neutral) from these reviews. Additionally, users can search for banks and specific services to get a sentiment-based summary of how customers feel about those services, represented visually through charts and diagrams.

Features

• Bank Reviews Sentiment Analysis: Analyze customer reviews and extract sentiment using an NLP-based machine learning model.
• Service Search: Search for banks and their services to view customer sentiment and ratings.
• Visualization: Interactive charts and diagrams displaying sentiment distribution, ratings, and service feedback.
• Machine Learning Model: Uses a pre-trained machine learning model for sentiment analysis on textual data.
• Bank & Service Directory: Allows users to search for specific services offered by different banks.

Technologies Used

• Natural Language Processing (NLP): For text analysis and sentiment extraction.
• Machine Learning: Classification models for sentiment analysis.
• Web Framework: Django
• Data Visualization: Charts and diagrams using libraries like Matplotlib, and chartJS.
• Backend: Python
• Frontend: HTML, CSS, JavaScript, Bootstrap

Installation

Prerequisites

Make sure you have the following installed:

• Python 3.x
• pip (Python package manager)
• Django

Steps

1. Clone the repository:

   git clone https://github.com/your-username/your-repository-name.git
   cd your-repository-name

2. Create a virtual environment:

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

3. Install dependencies:

   pip install -r requirements.txt

4. Set up the database (if applicable):

   python manage.py migrate  

5. Run the application:

   python manage.py runserver  # Adjust based on your framework

6. Access the application: Open your browser and go to http://127.0.0.1:8000/.

Usage

1. Search for a bank: Use the search bar to look for a specific bank and service.
2. View sentiment analysis: The platform will display a sentiment analysis for the selected service.
3. Explore visualizations: Interactive charts show detailed breakdowns of customer feedback.

Data Flow

1. Data Collection: The platform collects customer reviews for banks from various sources (e.g., online forums, social media, or internal databases).
2. Text Preprocessing: Raw text is cleaned, tokenized, and prepared for the machine learning model.
3. Sentiment Analysis: The machine learning model analyzes the sentiment of each review (positive, negative, neutral).
4. Search: Users can search for banks and services, and the corresponding sentiment is displayed.
5. Visualization: Sentiment results are presented visually through charts and graphs for easy understanding.

Machine Learning Model

• The model is trained on a labelled dataset of bank reviews.
• It uses Naive Bayes, and BERT for classifying reviews into sentiment categories.

Feature Extraction: TF-IDF

We use TF-IDF (Term Frequency-Inverse Document Frequency) as a feature extraction technique to convert textual data into numerical form for the machine learning model.

Visualizations

• Sentiment Distribution: Pie charts or bar graphs showing the proportion of positive, negative, and neutral reviews.
• Service-Specific Feedback: Line charts or histograms tracking sentiment over time for particular services.
• Overall Bank Performance: Comparative charts for different banks, based on customer reviews and ratings.

Contributing

Contributions are welcome! Please follow these steps:

1. Fork the repository.
2. Create a new branch (git checkout -b feature/your-feature).
3. Make your changes.
4. Commit your changes (git commit -m 'Add some feature').
5. Push to the branch (git push origin feature/your-feature).
6. Open a pull request.