From Traditional to Advanced Machine Learning: A Comparative Study of Political Tweet Sentiment Analysis

Nharekkat, Vishnunath (2024) From Traditional to Advanced Machine Learning: A Comparative Study of Political Tweet Sentiment Analysis. Masters thesis, Dublin, National College of Ireland.

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Abstract

The growing influence of social media networks on political discourse requires advanced sentiment analysis to recognize better public viewpoints revealed in complicated and diverse textual data. Existing approaches typically struggle to stabilize computational effectiveness with the ability to capture contextual nuances in sentiment classification jobs. In this research, we investigate machine learning and deep learning strategies, for analyzing the sentiments using tweets. To evaluate high-dimensional data with complex linguistic patterns, we preprocessed the data and fine-tuned it for better results. The Outcomes suggest that while SVM attained an accuracy of 85.91% because of its performance in structured data LSTM outmatched a little with an accuracy of 86.34%, succeeding at capturing nuanced linguistic features. From these results, we can understand that LSTM is better suited for sentiment analysis.

Item Type:	Thesis (Masters)
Supervisors:	Name Email Jilani, Musfira UNSPECIFIED
Subjects:	J Political Science > JA Political science (General) Q Science > QA Mathematics > Electronic computers. Computer science T Technology > T Technology (General) > Information Technology > Electronic computers. Computer science P Language and Literature > P Philology. Linguistics > Computational linguistics. Natural language processing Q Science > Q Science (General) > Self-organizing systems. Conscious automata > Machine learning Z Bibliography. Library Science. Information Resources > ZA Information resources > ZA4150 Computer Network Resources > The Internet > World Wide Web > Websites > Online social networks T Technology > TK Electrical engineering. Electronics. Nuclear engineering > Telecommunications > The Internet > World Wide Web > Websites > Online social networks
Divisions:	School of Computing > Master of Science in Data Analytics
Depositing User:	Ciara O'Brien
Date Deposited:	03 Sep 2025 15:15
Last Modified:	03 Sep 2025 15:15
URI:	https://norma.ncirl.ie/id/eprint/8760

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