Comparative analysis of sentiment classification using machine learning techniques on Twitter data

M. S. Kalaivani; S. Jayalakshmi; R. Priya

doi:10.53730/ijhs.v6nS2.7098

Authors

M. S. Kalaivani
kalaims2007@gmail.com
PhD, Computer Science at Vels Institute of Science, Technology and Advanced studies (VISTAS), Pallavaram & Assistant Professor in the Department of Computer Applications, Tagore College of Arts and Science, Chromepet, Chennai
S. Jayalakshmi Associate Professor at Vel Tech Multi Tech Dr.Rangarajan Dr.Sakunthala Engineering College (VTMT), Chennai
R. Priya Professor and Research Supervisor in the department of computer applications VISTAS ( Vels University) Chennai

Keywords:

sentiment analysis, machine learning techniques, support vector machine, Twitter

Abstract

Sentiment analysis, a track of Natural language processing field, which is used to categorize the online content into positive, negative and neutral comments. In this pandemic situation people order various products through online and there is an option to share their feedback. People refer ratings and reviews of other customers, before buy the product .The ecommerce field has reached next level of growth .The opinion or view of the customers play vital role in the growth of a business. The organization analyzes negative comments and predicts expectation of the customers, to develop their business. With the help of this analysis, effective decisions can be made to manage critical situations in business. In recent years various methods and techniques are used to analyze customer views. Machine learning techniques are well suited for sentiment analysis and achieved effective results .In this paper, support vector machine and Naive Bayes methods are used in sentiment classification with twitter data.

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