Predictive Modeling of Public Opinion for Karnataka Elections using Twitter Data Analysis
- DOI
- 10.2991/978-94-6463-250-7_30How to use a DOI?
- Keywords
- —LSTM; Twitter; Karnataka Elections; Deep Learning models; Machine Learning
- Abstract
The Karnataka state assembly elections have brought a new perspective to the political landscape, with diverse candidates representing various parties vying for the people's choice. This research work provides new insights into the predictive aspects of the Karnataka elections. Through the use of Deep Learning models applied to data collected from Twitter, the sentiments and past experiences of the ruling party were analyzed to predict the future elected party. The focus is on the public's sentiments with regards to the hopes for democratic policies in the future. The data was collected through the Twitter API and transformed into a well-structured format for training the model with a 70:30 ratio. The performance metric of the model is tabulated. Data exhibits mixture of opinions from the people of Karnataka towards the ruling party. While some tweets expressed confidence in the change of government formation in the future, others expressed concerns on religion and anti-national statements in a sarcastic manner. The LSTM model produced results with an accuracy of 87%.
- Copyright
- © 2024 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.
Cite this article
TY - CONF AU - Annie Syrien AU - M. Hanumanthappa PY - 2023 DA - 2023/10/17 TI - Predictive Modeling of Public Opinion for Karnataka Elections using Twitter Data Analysis BT - Proceedings of the 6th International Conference on Intelligent Computing (ICIC-6 2023) PB - Atlantis Press SP - 172 EP - 177 SN - 2352-538X UR - https://doi.org/10.2991/978-94-6463-250-7_30 DO - 10.2991/978-94-6463-250-7_30 ID - Syrien2023 ER -