Sentiment_analysis_of_Social_media_data_for_brand_monitoring

Overview

Welcome to the Sentiment Analysis . This project aims to provide a reliable system for sentiment analysis of social media data to track public opinion. By leveraging cutting-edge technologies like BERT (Bidirectional Encoder Representations from Transformers) and genetic algorithms, the system offers real-time insights to support brand management teams in making data-driven decisions.

Setup Instructions

• Python 3.x installed on your machine
• Pip package manager
• Virtual environment (optional but recommended)

Steps to Set Up

1. Clone the repository to your local machine:

   git clone https://github.com/your-repo/Sentiment_analysis_of_Social_media_data_for_brand_monitoring.git
   cd sentiment-analysis-brand-monitoring
   

2. Install the required dependencies:

   pip install -r requirements.txt
   

3. Download the pre-trained BERT model and place it in the models/ directory.

4. Run the application:

   python app.py
   

5. Access the application through your web browser at http://localhost:5000.

6. Requirements VScode, Python, Django, Trasformer, BertTokenizer

7. I have uploded dataset which can be used to run the output

Approach

• Utilizing BERT models for advanced natural language processing tasks.
• Implementing genetic algorithms to optimize sentiment analysis processes.
• Developing a user-friendly web interface for seamless interaction.

Feedback and Support

If you encounter any issues or have suggestions for improvement, please feel free to reach out to us at [[email protected]].

Thank you for using our Sentiment Analysis for Brand Monitoring application!