Political Sentiment Analysis & Outcome Prediction
Budget: ₹1,500 – ₹12,500 INR
Problem Statement
Exploring Changes in Public Opinion, Political Campaigns, and Political Behavior and Manipulation Detection through Transformers
Key Components of Problem Statements:
Public Sentiment Analysis: Transformers are well suited models to track how the public feels about politicians, policies, and political events through social media tweets
Predict Political Outcomes: Machine learning models to forecast election results and campaign effectiveness.
Misinformation Mitigation: Fact check analysis to detect and reduce the spread of fake news using Transformers
Policy Effectiveness Evaluation: Analyze social sentiment, public feedback, and economic indicators to differentiate between popular and truly effective policies and enhancing the transparency of political processes and ensuring accountability through data-driven insights.
Hybrid Approach : RoBERTa with LightGBM Regression
The model follows a hybrid design approach by combining fine-tuned RoBERTa for sentiment analysis and misinformation detection with LightGBM Regression for election prediction.
Fine-tuned RoBERTa for Sentiment Analysis & Misinformation Detection:
Trained on political text data to understand public sentiment and detect fake news.
Captures complex language patterns, sarcasm, and political bias effectively.
Provides context-aware and high-accuracy predictions compared to traditional ML methods.
LightGBM Regression for Election Prediction:
Uses sentiment trends extracted from RoBERTa as input features.
A lightweight yet powerful gradient-boosting model for numerical prediction.
Handles large-scale structured data efficiently while maintaining interpretability.
Enhanced Hybrid Model (Fine-Tuned RoBERTa + LightGBM with Feature Selection)
This improved approach extracts optimized embeddings from a fine-tuned RoBERTa model and uses them as input features for LightGBM. This balances accuracy, interpretability, and computational efficiency.
Benefits of the Enhanced Hybrid Model:
Higher Accuracy: Unlike frozen RoBERTa embeddings, fine-tuned embeddings are task-specific, improving classification and prediction performance.
Faster Training: LightGBM handles structured data efficiently, while only the final layers of RoBERTa are fine-tuned, reducing overall training time.
Improved Interpretability: LightGBM provides feature importance scores, helping understand which textual attributes influence predictions.
Better Scalability: Works well for medium to large datasets, as LightGBM generalizes efficiently even with moderate data sizes.
Optimized Computational Cost: Instead of end-to-end fine-tuning for each task, RoBERTa is partially fine-tuned for text embeddings, making it less
MODULES
Data Collection Module: Scrapers + APIs (ECI, LokDhaba, Twitter, NewsAPI).
Data Preprocessing Module: Cleaning, normalization, constituency mapping.
Feature Engineering Module: Funding strength, turnout impact, sentiment scores, historical swing.
Model Training Module: ML (XGBoost & Random Forest) + NLP (Sentiment).
Prediction Module: Win probabilities, seat share forecast.
Visualization Module: News Web Application using Next.JS as frontend
Python as backend and NoSQL DB
Backend & Processing: Python, Pandas, Scikit-learn, XGBoost, Hugging Face Transformers.
Datasets: Tweets, Previous Year Election Data, Electoral Bond and Voter turnover (CSV formats)
Middleware / APIs:
Twitter API v2, NewsAPI, NTScrapper
ECI bond data and Lok Dhaba datasets
Frontend: Next.js framework with HTML and Tailwind CSS.
Database : NoSQL database
Deployment: Docker containerisation or orchestration (Kubernetes)
Testing Levels
Unit Testing → Validate individual modules (scraper, NLP sentiment, feature calculations).
Integration Testing → Validate data pipeline from raw input → features → predictions.
System Testing → Validate full end-to-end prediction & dashboard.
Validation Testing → Backtest predictions against past election outcomes.
Exploring Changes in Public Opinion, Political Campaigns, and Political Behavior and Manipulation Detection through Transformers
Key Components of Problem Statements:
Public Sentiment Analysis: Transformers are well suited models to track how the public feels about politicians, policies, and political events through social media tweets
Predict Political Outcomes: Machine learning models to forecast election results and campaign effectiveness.
Misinformation Mitigation: Fact check analysis to detect and reduce the spread of fake news using Transformers
Policy Effectiveness Evaluation: Analyze social sentiment, public feedback, and economic indicators to differentiate between popular and truly effective policies and enhancing the transparency of political processes and ensuring accountability through data-driven insights.
Hybrid Approach : RoBERTa with LightGBM Regression
The model follows a hybrid design approach by combining fine-tuned RoBERTa for sentiment analysis and misinformation detection with LightGBM Regression for election prediction.
Fine-tuned RoBERTa for Sentiment Analysis & Misinformation Detection:
Trained on political text data to understand public sentiment and detect fake news.
Captures complex language patterns, sarcasm, and political bias effectively.
Provides context-aware and high-accuracy predictions compared to traditional ML methods.
LightGBM Regression for Election Prediction:
Uses sentiment trends extracted from RoBERTa as input features.
A lightweight yet powerful gradient-boosting model for numerical prediction.
Handles large-scale structured data efficiently while maintaining interpretability.
Enhanced Hybrid Model (Fine-Tuned RoBERTa + LightGBM with Feature Selection)
This improved approach extracts optimized embeddings from a fine-tuned RoBERTa model and uses them as input features for LightGBM. This balances accuracy, interpretability, and computational efficiency.
Benefits of the Enhanced Hybrid Model:
Higher Accuracy: Unlike frozen RoBERTa embeddings, fine-tuned embeddings are task-specific, improving classification and prediction performance.
Faster Training: LightGBM handles structured data efficiently, while only the final layers of RoBERTa are fine-tuned, reducing overall training time.
Improved Interpretability: LightGBM provides feature importance scores, helping understand which textual attributes influence predictions.
Better Scalability: Works well for medium to large datasets, as LightGBM generalizes efficiently even with moderate data sizes.
Optimized Computational Cost: Instead of end-to-end fine-tuning for each task, RoBERTa is partially fine-tuned for text embeddings, making it less
MODULES
Data Collection Module: Scrapers + APIs (ECI, LokDhaba, Twitter, NewsAPI).
Data Preprocessing Module: Cleaning, normalization, constituency mapping.
Feature Engineering Module: Funding strength, turnout impact, sentiment scores, historical swing.
Model Training Module: ML (XGBoost & Random Forest) + NLP (Sentiment).
Prediction Module: Win probabilities, seat share forecast.
Visualization Module: News Web Application using Next.JS as frontend
Python as backend and NoSQL DB
Backend & Processing: Python, Pandas, Scikit-learn, XGBoost, Hugging Face Transformers.
Datasets: Tweets, Previous Year Election Data, Electoral Bond and Voter turnover (CSV formats)
Middleware / APIs:
Twitter API v2, NewsAPI, NTScrapper
ECI bond data and Lok Dhaba datasets
Frontend: Next.js framework with HTML and Tailwind CSS.
Database : NoSQL database
Deployment: Docker containerisation or orchestration (Kubernetes)
Testing Levels
Unit Testing → Validate individual modules (scraper, NLP sentiment, feature calculations).
Integration Testing → Validate data pipeline from raw input → features → predictions.
System Testing → Validate full end-to-end prediction & dashboard.
Validation Testing → Backtest predictions against past election outcomes.