Twitter Emoticon Interpretation Using Machine Learning Algorithms in Sentiment Analysis

Year : 2024 | Volume :11 | Issue : 01 | Page : 1-6
By

R. Pushpa

P. Priyadarshani

S. Santhosh Kumar

  1. Student Department of Artificial Intelligence and Data Science, Sri Manakula Vinayagar Engineering College, Madagadipet Puducherry India
  2. Student Department of Artificial Intelligence and Data Science, Sri Manakula Vinayagar Engineering College, Madagadipet Puducherry India
  3. Student Department of Artificial Intelligence and Data Science, Sri Manakula Vinayagar Engineering College, Madagadipet Puducherry India

Abstract

In the current era, thousands of people share their opinions every day on the well-known microblogging platform Twitter in the form of tweets. A tweet must be brief and straightforward in order to be effective, though sentiment analysis of Twitter data will be the main emphasis of this study. Sentiment analysis study encompasses NLP and text data mining. We will conduct sentiment analysis on Twitter data using several logistic machine learning approaches. Nonetheless, our attention will be directed toward methods and varieties of sentiment analysis in which we will learn how to retrieve tweets from Twitter. In addition, we will uncover some common metrics and compare various machine learning approaches on the same dataset.

Keywords: Twitter, sentiment analysis (SA), machine learning, logistic regression, positive, negative, natural language processing, TfidfVectorizer, stemming

[This article belongs to Journal of Software Engineering Tools & Technology Trends(josettt)]

How to cite this article: R. Pushpa, P. Priyadarshani, S. Santhosh Kumar. Twitter Emoticon Interpretation Using Machine Learning Algorithms in Sentiment Analysis. Journal of Software Engineering Tools & Technology Trends. 2024; 11(01):1-6.
How to cite this URL: R. Pushpa, P. Priyadarshani, S. Santhosh Kumar. Twitter Emoticon Interpretation Using Machine Learning Algorithms in Sentiment Analysis. Journal of Software Engineering Tools & Technology Trends. 2024; 11(01):1-6. Available from: https://journals.stmjournals.com/josettt/article=2024/view=140109





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Regular Issue Subscription Review Article
Volume 11
Issue 01
Received February 29, 2024
Accepted March 28, 2024
Published April 5, 2024