Decoding Consumer Sentiment through Machine Learning: Analysing Social Media Trends and Behaviours
C.K. Kotravel Bharathi1, K. Elakkiyan2
1Dr. C.K. Kotravel Bharathi, Former Dean, SRM University – Tiruchirappalli Campus, Tamil Nadu, India.
2K. Elakkiyan, Final year B.Tech. (IT) Student, Vellalar College of Engineering and Technology, Erode, Tamil Nadu, India.
Manuscript received on 08 April 2025 | First Revised Manuscript received on 25 April 2025 | Second Revised Manuscript received on 04 May 2025 | Manuscript Accepted on 15 June 2025 | Manuscript published on 30 June 2025 | PP: 25-29 | Volume-14 Issue-7, June 2025 | Retrieval Number: 100.1/ijitee.G110214070625 | DOI: 10.35940/ijitee.G1102.14070625
Open Access | Editorial and Publishing Policies | Cite | Zenodo | OJS | Indexing and Abstracting
© The Authors. Blue Eyes Intelligence Engineering and Sciences Publication (BEIESP). This is an open access article under the CC-BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/)
Abstract: Social media platforms have become indispensable channels for public opinion, customer feedback, and brand perception. Analysing this vast repository of user-generated content enables businesses to gain deep insights into consumer sentiment and behaviour. This paper presents the “Social Media Sentiment Analyzer,” an interdisciplinary initiative combining Marketing and Information Technology to develop a machine learning-based tool for sentiment analysis. The tool processes social media posts to classify them as positive, negative, or neutral, offering organizations actionable insights for strategic decision-making.
Keywords: Consumer Sentiment, Machine Learning, NLP, Marketing, Behaviour Insights.
Scope of the Article: Data Analytics