Enhancing Sentiment Analysis in Social Media Using Blockchain Technology: a Subject Matter Archetype Approach
Raktim Kumar Dey, Debabrata Sarddar, Rajesh Bose, Shrabani Sutradhar, Sandip Roy · 2025
Analysis of sentiment helps businesses and organizations evaluate social media content from Twitter which allows them to track public sentiment and market trends. Normal sentiment analysis techniques encounter obstacles when handling data reliability concerns alongside data trust and data integrity problems. The research develops a new method to boost sentiment analysis of social media content through blockchain technology implementation. The research creates domain-specific subject matter architecture which achieves enhanced data integrity levels through blockchain implementations. Social media data collection forms the basis of the methodology followed by data preprocessing to reveal sentiment features which are used with machine learning algorithms for classification purposes. Blockchain implementation protects the proposed approach with its transparent and unalterable record storage to solve important issues in sentiment analysis. The tested solution shows enhanced accuracy together with improved reliability for social media analysis through blockchain-based structural implementations. The research adds new insights to the widespread blockchain-related research in sentiment analysis which benefits academic and professional practitioners working in this domain.