Unlocking Sentiment Insights on Twitter through Quantum Computing

Lateshwari, Sushil Kumar Bansal — Thu, 11 Jun 2026 00:00:00 +0000

This study looks at the role played by quantum influence and entangled by machine learning methods to improve sentiment analysis in social media type content. With the digital arena being on the verge of huge transformations, the interpretation of implicit feelings conveyed on the Internet has in recent years become an essential skill necessary for many areas of applications that include marketing efforts, public opinion analysis, and trend detection. Through the fusion of quantum computing principles alongside with state-of-the-art machine learning methods, our research work ultimately aims at that sensing sentiments permeate the entire social media ecosystem. We intend to span the idea gap between quantum mechanics and the feelings in the data analysis process, exploring the new path for understanding the complex emotions into the online interaction data. By implementing empirical analysis and experimental research, we strive to provide an illustration of the interdisciplinary nature of our methods and to eventually demonstrate the efficacy of this approach in reaching higher sentiment analysis accuracy and uncovering unknown ontological patterns within social media content. This will ultimately bring us closer to a better understanding of the dynamics of digital discourse.

DOI: https://doi.org/10.24321/3051.424X.202604

How to cite this article:
Lateshwari, Bansal S K. Unlocking Sentiment Insights on Twitter Through Quantum Computing. J Adv Res Comput Linguist : J Comput Sci Lang. 2026; 2(2): 1-9.

Journal of Advanced Research in Computational Linguistics: Journal of Computer Science Language

Unlocking Sentiment Insights on Twitter through Quantum Computing