BSc IT Project Guide: Twitter Sentiment Analysis
1. Project Overview
The 'Twitter Sentiment Analysis' project aims to analyze sentiments expressed in tweets related to specific topics, brands, or events. By leveraging natural language processing (NLP) techniques and sentiment analysis algorithms, the system categorizes tweets into positive, negative, or neutral sentiments. This project is valuable for businesses, researchers, and analysts who seek to understand public opinion and trends.
2. Objectives
- To collect tweets using Twitter API based on specific keywords or hashtags.
- To preprocess tweet data for analysis (tokenization, stopword removal, etc.).
- To classify tweet sentiments using machine learning or deep learning models.
- To visualize sentiment distribution and trends over time.
3. System Requirements
• Programming Language: Python
• Libraries: Tweepy, Pandas, NLTK/TextBlob/VADER, Matplotlib/Seaborn
• Tools: Jupyter Notebook, Google Colab or any IDE supporting Python
• Optional: Flask/Django for web interface
4. Modules Description
a. Tweet Collector: Fetch tweets using Twitter API.
b. Preprocessing Module: Clean tweets and prepare data for analysis.
c. Sentiment Analyzer: Classify sentiments using NLP models.
d. Visualization Module: Display results using graphs and charts.
5. Methodology
- Use Tweepy to fetch tweets.
- Clean text (remove emojis, mentions, URLs, etc.).
- Apply sentiment analysis using TextBlob or VADER.
- Store results and visualize data using charts.
6. Conclusion
This project helps in understanding public sentiment and social media trends. It can be extended to real-time analysis and integrated into dashboards for better decision-making.