AI-Powered Sentiment Analysis for Future Social Media Engagement

DOI

10.2991/978-94-6463-718-2_11How to use a DOI?

Keywords

AI sentiment analysis; deep learning sentiment; social media sentiment analysis; explainable AI; aspect-based sentiment analysis

Abstract

Sentiment analysis allows you to understand how users are engaging with you on social media. Traditional sentiment analysis techniques, while effective in controlled environments, do not handle the dynamic nature of social media, resulting in a limited ability to predict engagement patterns. Our study presents an AI-enhanced sentiment analysis framework that utilizes high-performing deep learning models, such as transformer-based architectures, to achieve better performance in both sentiment classification and engagement prediction. In the age of big data, millions of users express their views on social media platforms daily, which creates a large repository of unstructured text data offering valuable insights into public opinion. The Proposed study also includes explainable AI (XAI) that builds the pathway towards intelligibility, which helps to ensure clarity of sentiment classification results. The framework provides a comprehensive solution that can readily address cross-domain sentiment challenges and integrate emerging social media trends, resulting in a scalable, adaptive, and business-oriented approach to the optimization of social media engagement strategies. This study’s results show how AI-driven sentiment analysis can be used to predict user engagement and inform actionable insights for businesses, marketers and content creators.

Copyright

© 2025 The Author(s)

Open Access

Open Access This chapter is licensed under the terms of the Creative Commons Attribution-NonCommercial 4.0 International License (http://creativecommons.org/licenses/by-nc/4.0/), which permits any noncommercial use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license and indicate if changes were made.

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TY  - CONF
AU  - Shadab Pattekari
AU  - V. S. Thiyagarajan
AU  - V. P. Rameshkumaar
AU  - Pradnya Purandare
AU  - R. Reka
AU  - Riyazuddin Y. Md
PY  - 2025
DA  - 2025/05/23
TI  - AI-Powered Sentiment Analysis for Future Social Media Engagement
BT  - Proceedings of the International Conference on Sustainability Innovation in Computing and Engineering (ICSICE 2024)
PB  - Atlantis Press
SP  - 112
EP  - 124
SN  - 2352-538X
UR  - https://doi.org/10.2991/978-94-6463-718-2_11
DO  - 10.2991/978-94-6463-718-2_11
ID  - Pattekari2025
ER  -