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Emerging Trends in Machine Learning, Data Science, and Internet of Things

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From Tweets to Insights: A Diverse Classifier Approach to Analyze Sentiments on Twitter

Author(s): R. Sanmugasundaram*, Kaviraj G. M., Chitra M., Dileepan D., Manikandan M. and Kanimozhi J.

Pp: 38-57 (20)

DOI: 10.2174/9798898814717126010007

* (Excluding Mailing and Handling)

Abstract

This study delves into the realm of Twitter sentiment analysis, employing an array of machine learning algorithms. Through meticulous preprocessing of a substantial Twitter dataset, the research meticulously examines algorithmic performance. Results are elucidated through visual representations, revealing comparative efficacy, strengths, and limitations. This exploration significantly advances the comprehension of sentiment analysis within the context of social media, providing valuable insights for subsequent research and the refinement of methodologies in this dynamic domain. The study's findings contribute to the evolving discourse on sentiment analysis and its applications in the digital landscape.

Keywords: Comparative analysis, Feature selection, Machine learning algorithms, Twitter sentiment analysis, Text mining.

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