Exploring the Effectiveness of Various Machine Learning Models in Analyzing Sentiment from Twitter Data

13 pages•Published: August 6, 2024

Jishnu Prakash Kurukkat, Liz Maria Liyons, Anaswer Ajay, Akshaj Vadakkath Joshy and Sarath Sasidharan

Abstract

This research assesses sentiment analysis on Twitter posts about Twitter tweets, comparing ma- chine learning techniques. Four different models are used to classify and their efficacy is evaluated. Addressing balanced datasets, SVM excels in balanced sentiment scenarios. Using metrics like accuracy and recall, the study offersinsights for decision-making in marketing and social studies. Emphasizing machine learning effectiveness, the research suggests improvements for sentiment analysis in diverse domains, particularly in understanding positive and negative Twitter tweets.

Keyphrases: classification, comparative analysis, machine learning, svm

In: Rajakumar G (editor). Proceedings of 6th International Conference on Smart Systems and Inventive Technology, vol 19, pages 144-156.

Links:	https://easychair.org/publications/paper/JMjB
	https://doi.org/10.29007/47qj

BibTeX entry

@inproceedings{ICSSIT2024:Exploring_Effectiveness_Various_Machine,
  author    = {Jishnu Prakash Kurukkat and Liz Maria Liyons and Anaswer Ajay and Akshaj Vadakkath Joshy and Sarath Sasidharan},
  title     = {Exploring the Effectiveness of Various Machine Learning Models in Analyzing Sentiment from Twitter Data},
  booktitle = {Proceedings of 6th International Conference on Smart Systems and Inventive Technology},
  editor    = {Rajakumar G},
  series    = {Kalpa Publications in Computing},
  volume    = {19},
  publisher = {EasyChair},
  bibsource = {EasyChair, https://easychair.org},
  issn      = {2515-1762},
  url       = {/publications/paper/JMjB},
  doi       = {10.29007/47qj},
  pages     = {144-156},
  year      = {2024}}

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