PulseAI: A Real-Time Public Sentiment Analysis Framework Using Transformer-based Language Models on Customized Data
Keywords:
Aspect-based sentiment analysis, BERT, Deep learning, FastAPI, Machine learning, Natural language processing (NLP), React.js, Real-time analytics, RoBERTa, Sentiment analysis, Social media mining, Transformer modelsAbstract
In today’s digital world, social media platforms generate massive amounts of real-time textual data that reflect public opinions, emotions, and trends. Analyzing this data efficiently has become essential for businesses, researchers, and decision-makers to understand customer behavior and public sentiment. This project presents PulseAI, a real-time public sentiment analysis framework that uses Transformer-based Natural Language Processing techniques for intelligent opinion mining across multiple online platforms. The system integrates data from sources such as Twitter/X, Reddit, YouTube, Instagram, and news platforms using asynchronous API-based data collection mechanisms. The proposed framework employs a fine-tuned RoBERTa/BERT model for sentiment classification and contextual understanding of textual data. Unlike traditional machine learning approaches, the transformer-based architecture effectively captures semantic relationships, contextual dependencies, and sarcasm in user-generated content. The system performs sentiment classification into Positive, Negative, and Neutral categories while also providing emotion detection and aspect-based sentiment analysis. A FastAPI backend processes the incoming data efficiently, while a React-based dashboard visualizes sentiment trends, platform-wise analysis, heatmaps, word clouds, and real-time analytics. Experimental evaluation demonstrates that the proposed PulseAI framework achieves high performance with an accuracy of approximately 94%, outperforming conventional models such as Naïve Bayes and LSTM. The framework provides scalable, accurate, and real-time sentiment intelligence that can support applications in business analytics, brand monitoring, market research, and public opinion analysis. Future enhancements may include multilingual sentiment analysis and multimodal AI techniques combining text and image understanding.
References
J. Devlin, M.-W. Chang, K. Lee, and K. Toutanova, “BERT: Pre-training of deep bidirectional transformers for language understanding,” Proceedings of the 2019 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, vol. 1, 2019, pp. 4171–4186.
A. Vaswani et al., “Attention is all you need,” arXiv, 2017.
B. Pang, L. Lee and S. Vaithyanathan, “Thumbs up? Sentiment classification using machine learning techniques,” Proceedings of the 2002 Conference on Empirical Methods in Natural Language Processing, 2002, pp. 79–86.
R. E. Mitchell, Web scraping with Python: Collecting more data from the modern web, 2nd ed. Sebastopol, CA, USA: O'Reilly Media, 2018.
Y. Goldberg, Neural network methods in natural language processing. San Rafael, CA, USA: Morgan & Claypool Publishers, 2017.
T. Drugman, “Non-Linear Speech Processing (NOLISP 2013),” Computer Speech & Language, vol. 30, no. 1, pp. 1–2, Mar. 2015.
P. Goyal, S. Pandey, and K. Jain, Deep learning for natural language processing: creating neural networks with Python. Berkeley, California: Apress, New York, Ny, 2018.
T. Young, D. Hazarika, S. Poria and E. Cambria, “Recent trends in deep learning based natural language processing,” in IEEE Computational Intelligence Magazine, vol. 13, no. 3, pp. 55-75, Aug. 2018
T. Wolf et al., “Transformers: State-of-the-Art natural language processing,” Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing: System Demonstrations, Oct. 2020, pp. 38–45.
B. Liu, Sentiment analysis and opinion mining. San Rafael, CA, USA: Morgan & Claypool Publishers, 2012.
