Conceding Sentiment Prognosis on Twitter Data


  • Anshu Malhotra Department of Applied Sciences, The NorthCap University, Gurugram, India 122017
  • Nishu Sethi Department of Computer Science and Engineering, School of Engineering, The NorthCap University, Gurugram, Haryana, India 122017



Sentiment Analysis, Logistic Regression, Support Vector Machines, Opinion Mining, Supervised Machine Learning, Naïve Bayes, Decision Tress, Accuracy, Polarity Prediction


Twitter is the biggest micro-blogging website that gives people a platform to share their opinions about any new happenings around the world. The size of tweets is generally short which makes it very suitable for opinion mining. The key focus of the paper is to analyze the feelings and ideas. In this paper, analysis is done on the classification of tweets on a particular keyword. The tweets related to the given keyword are collected, analyzed, and the result is generated in the form of percentage of positive, neutral and negative sentiments, which gives us a sense of overall sentiment of the keyword. Further, Classification is done using supervised learning algorithms and the best among these will be found by calculating the accuracy of each.


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How to Cite

A. Malhotra and N. Sethi, “Conceding Sentiment Prognosis on Twitter Data”, J. Comput. Mech. Manag, vol. 3, no. 1, pp. 15–21, Feb. 2024.



Original Articles


Received 2023-11-14
Accepted 2023-12-04
Published 2024-02-29