Naïve Bayes Classification of Sentiments on Subset using Tweets-during Covid-19

Authors

  • V. Geetha, N. Sujatha, Latha Narayanan Valli

Keywords:

Social Media Data, Twitter, Sentiment Analysis, Machine Learning Algorithms, naïve Bayes Algorithm, Text Processing

Abstract

Now a day, Social Media create a platform for almost all people for sharing and communicating with one another. Most of the business people and the organizations avail the social media conversation for their product promotion or predicting people behavior. The popular Social Media Networks are Facebook, Twitter, LinkedIn of Social Networks, Instagram, YouTube of Media Sharing Networks, Whatsapp, Pinterest and tripAdvisor of Consumer Review Networks. A Text Mining tool, Sentiment Analysis can help us to predict and classify the susceptible text used in the social media conversation. Even though having lots of advantages, unfortunately we have many risks in the usage of the social media content. Any individual must follow the rules and regulations for accessing the content in the social media networks. The objective of this research paper is to understand the various techniques involved in Sentiment Analysis process and choose to apply naïve bayes machine learning model in the subset level using twitter data to classify the sentiments of people in a best way.

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Published

26.03.2024

How to Cite

N. Sujatha, Latha Narayanan Valli, V. G. . (2024). Naïve Bayes Classification of Sentiments on Subset using Tweets-during Covid-19. International Journal of Intelligent Systems and Applications in Engineering, 12(21s), 249–255. Retrieved from https://ijisae.org/index.php/IJISAE/article/view/5416

Issue

Vol. 12 No. 21s (2024)

Section

Research Article

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