Detecting Significant Behaviour in Tweets Using Machine Learning

EasyChair Preprint 13488, version 1

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.

Keyphrases: Sentiment Analysis, Tweets, hate speech, insert, political opinion

@booklet{EasyChair:13488,
  author    = {Faisal Shehzad and Muhammad Asad Ullah and Muhammad Adnan Khan and Nouh Sabri Elmitwally},
  title     = {Detecting Significant Behaviour in Tweets Using Machine Learning},
  howpublished = {EasyChair Preprint 13488},
  year      = {EasyChair, 2024}}