Poll Prediction Using Sentiment Analysis: Perspectives from Social Media Posts

DOI

10.2991/978-94-6239-789-7_45How to use a DOI?

Keywords

Sentiment Analysis; NLP; Social Media; Optimization

Abstract

The exponential development in social media platforms has altered the way people voice their opinion, feelings, and political positions, with the resulting enormous amount of user-generated data that can be used for data-driven decision-making. Sentiment analysis — one of the important techniques used in natural language processing — allows for the systematic extraction and interpretation of public sentiment expressed in texts shared on social media. This research examines the use of sentiment analysis on platforms such as Twitter (X), Facebook and Instagram for the purpose of extracting meaningful information to assist in the prediction of election results and political trends. The goal of the research, which analyzes large-scale social media data, is to infer voter preferences, ideological leanings, and collective political attitudes in near real-time. The study critically analyzes current methods of sentiment analysis, such as lexicon-based, machine learning, and deep learning methods, that are most suitable for the political domain, along with their advantages and disadvantages. Additionally, it deals with important problems such as data noise, sarcasm, misinformation, demographic bias and data privacy related ethical issues. The paper points out that there is a lack of research that could help increase the accuracy of election forecasting by incorporating multimodal data sources, advanced contextual modeling, and sentiment fusion across platforms. Overall, the work lays a scientific basis for improving the reliability of sentiment-driven election prediction systems via social media analytics.

Copyright

© 2026 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  - Manuraj Tomar
AU  - Rambir Singh
PY  - 2026
DA  - 2026/10/08
TI  - Poll Prediction Using Sentiment Analysis: Perspectives from Social Media Posts
BT  - Proceedings of theInternational Conference on Innovation and Practices in Artificial Intelligence (ICIPAI 2026)
PB  - Atlantis Press
SP  - 680
EP  - 691
SN  - 1951-6851
UR  - https://doi.org/10.2991/978-94-6239-789-7_45
DO  - 10.2991/978-94-6239-789-7_45
ID  - Tomar2026
ER  -