International Journal of Innovative Research in Computer and Communication Engineering
ISSN Approved Journal | Impact factor: 8.771 | ESTD: 2013 | Follows UGC CARE Journal Norms and Guidelines
| Monthly, Peer-Reviewed, Refereed, Scholarly, Multidisciplinary and Open Access Journal | High Impact Factor 8.771 (Calculated by Google Scholar and Semantic Scholar | AI-Powered Research Tool | Indexing in all Major Database & Metadata, Citation Generator | Digital Object Identifier (DOI) |
| TITLE | Agentic AI for Automated Sentiment Analysis |
|---|---|
| ABSTRACT | In the digital era, manually monitoring public sentiment on platforms like Reddit is inefficient and non-scalable. This project, "Agentic AI for Automated Sentiment Analysis," presents an intelligent, end-to-end solution that automates the transformation of social media data into actionable business intelligence.The system employs a multi-agent workflow orchestrated by LangGraph, where specialized agents perform data collection, sentiment analysis, and summarization. Using the Reddit API and NLTK VADER, it classifies posts by sentiment. The BART transformer model then generates distinct summaries for each category. Encapsulated in a full-stack Flask and Streamlit application, the system provides a "single-click" analytical experience. This integration of NLP, AI summarization, and agentic orchestration delivers a robust tool for real-time sentiment monitoring, empowering organizations in marketing, PR, and product management to make faster, data-driven decisions. |
| TITLE | |
| AUTHOR | ANANYA S, YASHASWINI S, SANDHYA G, M V JAGANNATHA REDDY Student, Dept. of Artificial Intelligence and Machine Learning, MS Engineering College, Bengaluru, Karnataka, India HOD, Dept. of Artificial Intelligence and Machine Learning, MS Engineering College, Bengaluru, Karnataka, India Guide, Dept. of Artificial Intelligence and Machine Learning, MS Engineering College, Bengaluru, Karnataka, India |
| VOLUME | 182 |
| DOI | DOI: 10.15680/IJIRCCE. 2026.1403038 |
| pdf/38_Agentic AI for Automated Sentiment.pdf | |
| KEYWORDS | |
| References | 1. Hutto, C. J., & Gilbert, E. E. (2014). VADER: A Parsimonious Rule- based Model for Sentiment Analysis of Social Media Text. Proceedings of the Eighth International Conference on Weblogs and Social Media, 216- 225. 2. Lewis, M., Liu, Y., Goyal, N., Ghazvininejad, M., Mohamed, A., Levy, O., & Zettlemoyer, L. (2019). BART: Denoising Sequence-to- Sequence Pre-training for Natural Language Generation, Translation, and Comprehension. arXiv preprint arXiv:1910.13461. https://arxiv.org/abs/1910.13461 https://api.slack.com/messaging/we bhooks. 3. Vaswani, A., Shazeer, N., Parmar, N., Uszkoreit, J., Jones, L., Gomez, A. N., & Polosukhin, I. (2017). Attention Is All You Need. Advances in Neural Information Processing Systems, 30. 4. Liu, B. (2012). Sentiment Analysis and Opinion Mining. Synthesis Lectures on Human Language Technologies, 5(1), 1-167. 5. Kumar, S., Morstatter, F., & Liu, H. (2018). Twitter Data Analytics. Springer International Publishing. 6. LangChain Inc. (2023). LangGraph: Stateful Workflow Orchestration for AI Agents. Official Documentation. https://langchain- ai.github.io/langgraph/ 7. Hugging Face (2023). Transformers: State-of-the-art Machine Learning for Pytorch, TensorFlow, and JAX. https://huggingface.co/docs/transfo rmers/ 8. Slack Technologies. (2023). Slack API: Webhook Integration Guide. https://api.slack.com/messaging/we bhooks. 9. Slack Technologies. (2023). Slack API: Webhook Integration Guide. 10. D. Sandhya G., “Applications of Artificial Intelligence in Social Media Analytics,” International Journal of Advanced Research in Computer Science, vol. 14, no. 2, pp. 45–50, 2023. |
