Research Article
HAGE AI – Sentiment Analysis Using GenAI
@INPROCEEDINGS{10.4108/eai.28-4-2025.2357921, author={Vijayalakshmi N and Lavanya M and Akshara Murali and Eniya R and Gurunathan S and Hari Shivam S}, title={HAGE AI -- Sentiment Analysis Using GenAI}, proceedings={Proceedings of the 4th International Conference on Information Technology, Civil Innovation, Science, and Management, ICITSM 2025, 28-29 April 2025, Tiruchengode, Tamil Nadu, India, Part I}, publisher={EAI}, proceedings_a={ICITSM PART I}, year={2025}, month={10}, keywords={sentiment analysis generative ai aspect-based sentiment analysis social media analytics ai agents llm orchestration customer feedback analysis hage (high accuracy generalized emotion)}, doi={10.4108/eai.28-4-2025.2357921} }- Vijayalakshmi N
Lavanya M
Akshara Murali
Eniya R
Gurunathan S
Hari Shivam S
Year: 2025
HAGE AI – Sentiment Analysis Using GenAI
ICITSM PART I
EAI
DOI: 10.4108/eai.28-4-2025.2357921
Abstract
User sentiment understanding has become more important for organizations as well as consumers because the use of social platform as a marketing place, a platform for brand engagement, and public discussion has increased at a very fast pace. Aspect-based emotions, contextual nuance, and multifaceted emotional expressions are occasionally hard for conventional sentiment analysis algorithms to interpret accurately. Also, these systems fail to provide companies with real-time information that could enhance consumer satisfaction and decision-making. HAGE-AI (High Accuracy Generalized Emotion) is an advanced sentiment analysis system based on a multi-agent framework and Generative AI for attaining social media analytics and high-accuracy emotion detection. HAGE-AI ensures an in-depth understanding of user sentiment through the application of different AI agents for sentiment classification, aspect-based sentiment analysis, interaction analysis, and comment summarization. Also, the system features an interactive chatbot that allows customers to inquire in real time regarding social engagement metrics, consumer feedback trends, and brand reputation.
