BSc IT Project Guide: Political Sentiment Analysis
1. Introduction
The Political Sentiment Analysis project aims to monitor and analyze public sentiment on political topics using natural language processing (NLP) and machine learning techniques. This tool can help stakeholders understand public opinion trends based on data from social media, news articles, forums, and other platforms.
2. Objectives
- To collect and preprocess political text data from various
sources.
- To apply sentiment analysis techniques to classify the sentiments.
- To visualize the sentiment trends over time or by topic.
- To develop a user-friendly dashboard for real-time monitoring.
3. Tools and Technologies
- Python
- Natural Language Toolkit (NLTK), spaCy
- Scikit-learn or TensorFlow/Keras
- Tweepy (for Twitter data), BeautifulSoup (for web scraping)
- Flask or Django (for web interface)
- Power BI / Matplotlib / Plotly (for visualization)
4. Methodology
1. Data Collection: Scrape or extract political data from
social media, news sites, etc.
2. Data Cleaning: Remove noise, stop words, and apply tokenization and
stemming.
3. Sentiment Classification: Train models using labeled datasets to classify
sentiments.
4. Analysis & Visualization: Display findings via charts and timelines.
5. Deployment: Develop a web-based dashboard for public or internal use.
5. Expected Outcomes
- An automated system for collecting and analyzing political
sentiment.
- Insightful visual reports showing sentiment trends.
- A deployable web application for stakeholder usage.
6. Conclusion
This project integrates machine learning, NLP, and data visualization to provide valuable insights into political sentiment. It demonstrates the practical application of data science in political and social domains.