https://github.com/kanishk3813/intel_sentiment_analysis

Intel Review Analyzer is a powerful tool designed to help businesses understand customer sentiments through automated analysis of reviews. This project leverages state-of-the-art NLP techniques to classify reviews, highlight key sentiments, generate word clouds, and visualize trends over time.
https://github.com/kanishk3813/intel_sentiment_analysis

axios bert-model cors deep-learning flask pandas python react spacy

Last synced: 4 months ago
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Intel Review Analyzer is a powerful tool designed to help businesses understand customer sentiments through automated analysis of reviews. This project leverages state-of-the-art NLP techniques to classify reviews, highlight key sentiments, generate word clouds, and visualize trends over time.

• Host: GitHub
• URL: https://github.com/kanishk3813/intel_sentiment_analysis
• Owner: Kanishk3813
• Created: 2024-07-08T10:07:12.000Z (7 months ago)
• Default Branch: main
• Last Pushed: 2024-07-21T13:53:34.000Z (7 months ago)
• Last Synced: 2024-10-14T04:02:54.687Z (4 months ago)
• Topics: axios, bert-model, cors, deep-learning, flask, pandas, python, react, spacy
• Language: Jupyter Notebook
• Homepage:
• Size: 10.4 MB
• Stars: 4
• Watchers: 1
• Forks: 1
• Open Issues: 0
• Metadata Files:
  • Readme: README.md

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README

        
# Intel Review Analyzer

## Overview

Intel Review Analyzer is a powerful tool designed to help businesses understand customer sentiments through automated analysis of reviews. This project leverages state-of-the-art NLP techniques to classify reviews, highlight key sentiments, generate word clouds, and visualize trends over time. **It works for both amazon and flipkart.**

## Features

- **Automated Sentiment Analysis**: Utilizes the BERT model to classify reviews into positive, neutral, or negative categories. Additionally, it highlights the positive and negative parts of each review and provides improvement suggestions based on negative feedback.

- **Word Cloud Generation**: Creates a visual representation of the most frequently mentioned words in the reviews, helping users quickly identify common themes and topics.

- **Past Trends Visualization**: Graphical representation of review sentiments over different periods, allowing businesses to track changes in customer perception over time.

- **CSV Upload**: Users can easily upload a CSV file containing reviews, enabling batch processing and analysis of large datasets.

- **Downloadable Reports**: Analyzed data can be downloaded in JSON format, providing users with detailed reports for further analysis and record-keeping.

## Technologies Used

### Frontend

- React

- Axios

- Chart.js

### Backend

- Flask

- Python

- BERT (Hugging Face Transformers)

### Data Handling

- Pandas

- Spacy

### Visualization

- WordCloud

- Matplotlib

### Training the Model

- PyTorch

- Transformers

- TensorFlow

## Installation

1. **Clone the repository**:

    ```bash

    git clone https://github.com/Kanishk3813/Intel_Sentiment_Analysis.git

    ```

2. **Backend Setup**:

    - Create a virtual environment and activate it:

        ```bash

        python -m venv venv

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

        ```

    - Install the required Python packages:

        ```bash

        pip install -r py_requirements.txt

        ```

    - **Download the BERT model**:

        - Download the BERT model from [this Google Drive link](https://drive.google.com/file/d/14QfV6USLLzlzb13_T8kFuGi6OmZppft2/view?amp;usp=embed_facebook) and place it inside the `backend/bert_model` folder.

    - **Create a .env file** in the `backend` folder with the following content:

        ```plaintext

        SCRAPER_API_KEY='ccceef77d6a524862c0c12aa202ff659'

        ```

3. **Frontend Setup**:

    - Navigate to the root directory and install the necessary packages:

        ```bash

        npm install

        ```

## Usage

### Running Locally

1. **Start the application**:

    ```bash

    npm start

    ```

## Demo





## Future Enhancements

- **Enhanced Sentiment Analysis**: Support for multi-language and emotion detection.

- **Real-Time Analysis**: Real-time review fetching and sentiment tracking.

- **User Feedback Integration**: Feedback loop for improved accuracy.

- **Advanced Visualization Tools**: Interactive and dynamic visualizations.

- **Social Media Integration**: Track sentiments from social media platforms.

- **Aspect-Based Sentiment Analysis**: Detailed aspect-based sentiment reports.

- **Predictive Analysis**: Predict future trends based on historical data.

## Contributing

Contributions are welcome!