Social Media Insights – Sentiment Analysis with LSTM, BERT, and GRU Models | CB1638 | Codebook.in

The exponential growth of social media platforms has generated vast volumes of user-generated textual data, necessitating advanced sentiment analysis systems capable of handling informal language, contextual nuances, and complex linguistic patterns. Traditional machine learning approaches, including Support Vector Machines, Decision Trees, and Naive Bayes, demonstrate moderate performance but often fail to capture sarcasm, context dependencies, and long-range semantic relationships inherent in social media content.

This project introduces the Hierarchical Contextual Ensemble (HCE) framework, developed as a full-stack web application using Python and the Django framework in accordance with IEEE academic standards. The system integrates transformer-based embeddings with recurrent neural network architectures to enhance sentiment classification accuracy.

HCE leverages fine-tuned BERT embeddings for contextual feature extraction, combined with LSTM and GRU models for sequential pattern learning. A stacking-based ensemble strategy is employed to select the optimal predictive model based on evaluation metrics such as accuracy, precision, recall, and F1-score. The framework is trained on a labeled dataset of social media entries to ensure robust performance across varied linguistic expressions.

The deployed web application includes OTP-based user authentication, real-time sentiment prediction, and feedback collection mechanisms. Administrative dashboards enable performance monitoring and iterative model refinement based on user input.

Designed strictly for academic and research purposes, this project demonstrates applied deep learning, ensemble modeling, and full-stack web deployment for advanced contextual sentiment analysis systems.

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B.E, B.Tech, MCA, MSc, M.E, M.Tech, BCA and BSc – Universities in India & Abroad

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DISCLAIMER:
This project is developed strictly for academic and research purposes following IEEE guidelines.

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