Mathematical Modelling and Implementation of NLP for Prediction of Election Results based on social media Twitter Engagement and Polls
Keywords:
Twitte, election result prediction, recursive neural tensor network, Natural Language ProcessingAbstract
Leveraging Twitter data for predicting election outcomes has become a notable trend in political research. Researchers use sentiment analysis tools to categorize tweets into positive, negative, or neutral sentiments. This helps gauge the public's overall mood regarding political candidates or issues. Analyzing sentiment patterns over time provides insights into the evolving dynamics of public opinion during the election cycle. Network analysis identifies influential users, key themes, and the structure of political discourse on Twitter. Integrating diverse data sources provides a more comprehensive and accurate picture of public Twitter data analysis can offer valuable insights for election campaigns, allowing them to tailor their strategies based on real-time public sentiment. Twitter users may only be partially representative of the general population. Identifying genuine sentiments from tweets can be challenging due to sarcasm, irony, and other nuances of language. Advancements in machine learning algorithms and natural language processing contribute to developing more accurate models.
Downloads
References
Olabanjo, Olusola, Ashiribo Wusu, Oseni Afisi, Mauton Asokere, Rebecca Padonu, Olufemi Olabanjo, Oluwafolake Ojo, Olusegun Folorunso, Benjamin Aribisala, and Manuel Mazzara. "From Twitter to Aso-Rock: A sentiment analysis framework for understanding Nigeria 2023 presidential election." Heliyon 9, no. 5 (2023).
Tripathi, Mukesh Kumar, M. Neelakantapp, Anant Nagesh Kaulage, Khan Vajid Nabilal, Sahebrao N. Patil, and Kalyan Devappa Bamane. "Breast Cancer Image Analysis and Classification Framework by Applying Machine Learning Techniques." International Journal of Intelligent Systems and Applications in Engineering 11, no. 3 (2023): 930-941.
Dhiman, Aarzoo, and Durga Toshniwal. "AI-based Twitter framework for assessing the involvement of government schemes in electoral campaigns." Expert Systems with Applications 203 (2022): 117338.
Tripathi, Mukesh Kumar, and Dhananjay D. Maktedar. "Internal quality assessment of mango fruit: an automated grading system with ensemble classifier." The Imaging Science Journal 70, no. 4 (2022): 253-272.
Orellana-Rodriguez, Claudia, and Mark T. Keane. "Attention to news and its dissemination on Twitter: A survey." Computer Science Review 29 (2018): 74-94.
Shivendra, Kasa Chiranjeevi, and Mukesh Kumar Tripathi. "Detection of Fruits Image Applying Decision Tree Classifier Techniques." In Computational Intelligence and Data Analytics: Proceedings of ICCIDA 2022, pp. 127-139. Singapore: Springer Nature Singapore, 2022.
Kondamudi, Medeswara Rao, Somya Ranjan Sahoo, Lokesh Chouhan, and Nandakishor Yadav. "A comprehensive survey of fake news in social networks: Attributes, features, and detection approaches." Journal of King Saud University-Computer and Information Sciences 35, no. 6 (2023): 101571.
Parackal, Mathew, Damien Mather, and David Holdsworth. "Value-based prediction of election results using natural language processing: A case of the New Zealand General Election." International Journal of Market Research 60, no. 2 (2018): 156-168.
Tripathi, Mukesh Kumar, Dhananjay Maktedar, D. N. Vasundhara, CH VKNSN Moorthy, and Preeti Patil. "Residual Life Assessment (RLA) Analysis of Apple Disease Based on Multimodal Deep Learning Model." International Journal of Intelligent Systems and Applications in Engineering 11, no. 3 (2023): 1042-1050.
Mohd, Mudasir, Saheeba Javeed, Mohsin Altaf Wani, Hilal Ahmad Khanday, Abid Hussain Wani, Umar Bashir Mir, and Sheikh Nasrullah. "poliWeet—Election prediction tool using tweets." Software Impacts 17 (2023): 100542.
Tripathi, Mukesh Kumar. "Neutrosophic approach based intelligent system for automatic mango detection." Multimedia Tools and Applications (2023): 1-23.
Parackal, Mathew, Damien Mather, and David Holdsworth. "Value-based prediction of election results using natural language processing: A case of the New Zealand General Election." International Journal of Market Research 60, no. 2 (2018): 156-168.
Tripathi, Mukesh Kumar, and Dhananjay D. Maktedar. "Optimized deep learning model for mango grading: Hybridizing lion plus firefly algorithm." IET Image Processing 15, no. 9 (2021): 1940-1956.
Moorthy, CH VKNSN, Mukesh kumar Tripathi, Nilesh Pandurang Bhosle, Vishnu Annarao Suryawanshi, Priyanka Amol Kadam, and Suvarna Abhimunyu Bahir. "Investigating Advanced and Innovative Non-Destructive Techniques to Grade the Quality of Mangifera Indica." International Journal of Intelligent Systems and Applications in Engineering 12, no. 1 (2024): 299-309.
Gayo-Avello, Daniel. "A meta-analysis of state-of-the-art electoral prediction from Twitter data." Social Science Computer Review 31, no. 6 (2013): 649-679.
Alvi, Quratulain, Syed Farooq Ali, Sheikh Bilal Ahmed, Nadeem Ahmad Khan, Mazhar Javed, and Haitham Nobanee. "On the frontiers of Twitter data and sentiment analysis in election prediction: a review." PeerJ Computer Science 9 (2023): e1517.
Tripathi, Mukesh Kumar, Praveen Kumar Reddy, and Madugundu Neelakantappa. "Identification of mango variety using near infrared spectroscopy." Indonesian Journal of Electrical Engineering and Computer Science 31, no. 3 (2023): 1776-1783.
Singh, Prabhsimran, Yogesh K. Dwivedi, Karanjeet Singh Kahlon, Annie Pathania, and Ravinder Singh Sawhney. "Can twitter analytics predict election outcome? An insight from 2017 Punjab assembly elections." Government Information Quarterly 37, no. 2 (2020): 101444.
Antonakaki, Despoina, Paraskevi Fragopoulou, and Sotiris Ioannidis. "A survey of Twitter research: Data model, graph structure, sentiment analysis and attacks." Expert Systems with Applications 164 (2021): 114006.
Georgiadou, Elena, Spyros Angelopoulos, and Helen Drake. "Big data analytics and international negotiations: Sentiment analysis of Brexit negotiating outcomes." International Journal of Information Management 51 (2020): 102048.
Tripathi, Mukesh Kumar, and Dhananjay D. Maktedar. "Optimized deep learning model for mango grading: Hybridizing lion plus firefly algorithm." IET Image Processing 15, no. 9 (2021): 1940-1956.
P. A. Gagniuc. Markov chains: from theory to implementation and experimentation. John Wiley & Sons, 2017.
D. I. Martin and M. W. Berry. “Mathematical foundations behind latent semantic analysis”. In. 2007.
M. Gerlach and F. Font-Clos. “A standardized Project Gutenberg corpus for statistical analysis of natural language and quantitative linguistics”. Entropy 22.1 (2020), p. 126.
T. Jolliffe and J. Cadima. “Principal component analysis: a review and recentdevelopments”. Philosophical Transactions of the Royal Society A: Mathematical, Physical and Engineering Sciences 374.2065 (2016), p.20150202.
Downloads
Published
How to Cite
Issue
Section
License
This work is licensed under a Creative Commons Attribution-ShareAlike 4.0 International License.
All papers should be submitted electronically. All submitted manuscripts must be original work that is not under submission at another journal or under consideration for publication in another form, such as a monograph or chapter of a book. Authors of submitted papers are obligated not to submit their paper for publication elsewhere until an editorial decision is rendered on their submission. Further, authors of accepted papers are prohibited from publishing the results in other publications that appear before the paper is published in the Journal unless they receive approval for doing so from the Editor-In-Chief.
IJISAE open access articles are licensed under a Creative Commons Attribution-ShareAlike 4.0 International License. This license lets the audience to give appropriate credit, provide a link to the license, and indicate if changes were made and if they remix, transform, or build upon the material, they must distribute contributions under the same license as the original.
