BSc IT Project Guide: Brand Sentiment Monitoring
1. Project Title
Brand Sentiment Monitoring System
2. Objective
To develop a system that analyzes and monitors public sentiment about specific brands in real-time using data from social media and news sources.
3. Tools & Technologies
• Programming Languages: Python
• Libraries: Tweepy, TextBlob, NLTK, Scikit-learn, pandas
• Database: MySQL or MongoDB
• Frontend: HTML, CSS, JavaScript
• Frameworks: Flask or Django
• APIs: Twitter API, News API
4. Functional Requirements
• Collect real-time data from Twitter and news sources.
• Preprocess text data (remove noise, stopwords, etc.).
• Perform sentiment analysis using NLP techniques.
• Categorize sentiment as positive, negative, or neutral.
• Visualize brand sentiment trends using graphs.
• Allow users to compare sentiment across brands.
5. Non-Functional Requirements
• Scalability to handle large data volumes.
• Real-time or near-real-time data processing.
• Responsive UI design.
• Secure API handling and data storage.
6. Project Modules
• Data Collection Module
• Data Cleaning & Preprocessing Module
• Sentiment Analysis Engine
• Brand Sentiment Dashboard
• User Management & Authentication Module
7. System Architecture
1. Data Sources (Twitter, News APIs) → 2. Data Ingestion → 3. NLP Processing → 4. Sentiment Classification → 5. Database → 6. Dashboard/Visualization
8. Expected Outcome
An interactive web platform where users can input brand names and view real-time sentiment analysis results across various channels, with visual trend indicators.
9. Future Scope
• Integration with more platforms like Reddit, Instagram.
• Advanced sentiment models using deep learning.
• Geolocation-based sentiment mapping.
10. References
• https://www.nltk.org/
• https://textblob.readthedocs.io/
• https://developer.twitter.com/
• https://newsapi.org/