BSc IT Project Guide: Real-Time Social Media Sentiment
1. Introduction
This project aims to develop a real-time sentiment analysis platform that monitors social media platforms (e.g., Twitter) during specific events such as product launches, sports games, or political events. The goal is to track public sentiment, analyze trends, and provide meaningful insights in real time.
2. Objectives
- Collect real-time social media data using APIs (e.g., Twitter API).
- Preprocess and clean the collected data.
- Perform sentiment analysis using machine learning or NLP models.
- Visualize sentiment trends and keyword frequencies in real time.
- Enable filtering by time, keyword, or sentiment.
3. Tools and Technologies
- Programming Language: Python
- APIs: Twitter API or other social media APIs
- Libraries: Tweepy, TextBlob, NLTK, scikit-learn, Pandas, Matplotlib,
Dash/Streamlit
- Database: SQLite or MongoDB
- Frontend: HTML/CSS or Streamlit for dashboard
4. System Architecture
1. Data Collection Module (via Twitter API)
2. Data Preprocessing Module (cleaning, tokenization, etc.)
3. Sentiment Analysis Engine (NLP/ML models)
4. Visualization Dashboard (real-time charts and insights)
5. Implementation Steps
1. Register and configure API access to collect tweets.
2. Build a script to fetch tweets and store them in a database.
3. Clean the tweet text and apply preprocessing techniques.
4. Train or use a pre-trained sentiment analysis model.
5. Develop a dashboard to show live sentiment graphs.
6. Add features like filtering by keyword, date, or sentiment type.
6. Challenges
- Handling large volumes of data in real time.
- Dealing with noisy and unstructured text data.
- Maintaining accuracy in sentiment classification.
- Rate limiting and restrictions of APIs.
7. Future Enhancements
- Extend support to other platforms like Facebook or Reddit.
- Add multilingual sentiment analysis.
- Implement advanced ML models like BERT or LSTM.
- Include location-based sentiment insights.