Detecting Significant Behaviour in Tweets using Machine Learning

Shahzad, Faisal and Asad Ullah, Muhammad and Adnan Khan, Muhammad and Elmitwally, Nouh (2024) Detecting Significant Behaviour in Tweets using Machine Learning. In: 9th International Conference on Next Generation Computing (ICNGC 2023), 20th - 23rd December 2023, Danang, Vietnam.

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Abstract

Sentiment Analysis is a crucial area of study within the realm of Computer Science. With the rapid advancement of Information Technology and the prevalence of social media, a substantial volume of textual comments has emerged on web platforms and social networks such as Twitter. Consequently, individuals have become increasingly active in disseminating both general and politically-related information, making it imperative to examine public responses. Many researchers have harnessed the unique features and content of social media to assess and forecast public sentiment regarding political events. This study presents an analytical investigation employing data from general discussions on Twitter to decipher public sentiment regarding the crisis in Pakistan. It involves the analysis of tweets authored by various ethnic groups and influential figures using Machine Learning techniques like the Support Vector Classifier (SVC), Decision Tree (DT), Naïve Bayes (NB) and Logistic Regression. Ultimately, a comparative assessment is conducted based on the outcomes obtained from different models in the experiments.

Item Type:	Conference or Workshop Item (Paper)
Dates:	Date Event 28 November 2023 Accepted 31 May 2024 Published Online
Uncontrolled Keywords:	hate speech, sentiment analysis, tweets, political opinion, insert.
Subjects:	CAH11 - computing > CAH11-01 - computing > CAH11-01-01 - computer science
Divisions:	Faculty of Computing, Engineering and the Built Environment > College of Computing
Depositing User:	Nouh Elmitwally
Date Deposited:	30 Nov 2023 13:29
Last Modified:	07 Jan 2025 09:33
URI:	https://www.open-access.bcu.ac.uk/id/eprint/15024

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