Copyright © 2026 Authors retain the copyright of this article. This article is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

@article{200956,
        author = {Jamakayala Vasanth Kumar and Kandyala Ganga Prathap and Keshetty Charan and Ms. D. Nandhini},
        title = {AI Powered Twitter Sentiment and Trend Intelligence System},
        journal = {International Journal of Innovative Research in Technology},
        year = {2026},
        volume = {12},
        number = {12},
        pages = {3140-3147},
        issn = {2349-6002},
        url = {https://ijirt.org/article?manuscript=200956},
        abstract = {The AI-Powered Twitter Sentiment and Trend Intelligence System is a web-based platform that analyzes live Twitter data using Artificial Intelligence and Natural Language Processing techniques. It performs multi-class sentiment classification through a hybrid architecture combining Transformer-based models and a custom Twitter-trained Machine Learning model. The system also supports emotion detection, AI-generated summaries, and real-time trend visualization via an interactive dashboard. Predictions are stored in a relational database with REST API support and secure user authentication. With 92.65% accuracy on custom models and 95%+ on Transformer models, the system enables intelligent, real-time social media monitoring and trend analysis the system integrates advanced Natural Language Processing (NLP) modules to provide deeper contextual insights. By utilizing Emotion Detection and AI-generated summaries, the platform transforms raw, unstructured social media data into actionable intelligence. This allows users to understand not just the general sentiment of a conversation, but the specific emotional drivers—such as joy, anger, or sadness—underlying public discourse. The real-time trend visualization dashboard further enhances this by providing a dynamic interface where stakeholders can monitor shifting public opinions as they happen. The technical foundation of the platform is designed for high performance and scalability. The hybrid architecture leverages the strengths of Transformer-based models for ennhanced understanding.},
        keywords = {Sentiment analysis, twitter data, data preprocessing, Deep Learning, Opinion Mining,},
        month = {May},
        }