SENTIMENT ANALYSIS OF PEOPLE DURING COVID- 19 USING SVM AND LOGISTIC REGRESSION ANALYSIS

Authors

  • Sayan Majumder Department of Computer Science & Engineering, Gargi Memorial Institute of Technology, Baruipur, Kolkata
  • Anuran Aich Department of Computer Science & Engineering, Gargi Memorial Institute of Technology, Baruipur, Kolkata
  • Satrajit Das Department of Computer Science & Engineering, Gargi Memorial Institute of Technology, Baruipur, Kolkata

Keywords:

Sentiment Analysis, NLP, Machine Learning, SVM, Regression.

Abstract

Starting from China, novel coronavirus is now the most dangerous threat to humans, all over the world. India is also not an exception. Besides using mask and sanitizers, Indian Government has also decided to maintain proper social distancing or lockdown. Our sentiment during this lockdown period also varied from man to man. In this paper, we have collected twitter data of people across India, during March to June and then using NLP, the polarity is measured, i.e. positive, negative or neutral. After that. We have used SVM classifier and Logistic Regression analysis to classify the sentiments of people. Python programing language is used in Anaconda distribution to simulate the result. After simulation, we have found that SVM gives 91.50% accuracy, whereas Logistic Regression gives 87.75% accuracy. At last, a comparative study between these two results, is also represented.

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Published

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How to Cite

Sayan Majumder, Anuran Aich, & Satrajit Das. (2022). SENTIMENT ANALYSIS OF PEOPLE DURING COVID- 19 USING SVM AND LOGISTIC REGRESSION ANALYSIS. EPRA International Journal of Multidisciplinary Research (IJMR), 8(6), 1–5. Retrieved from http://www.eprajournals.net/index.php/IJMR/article/view/491

Issue

Vol. 8 No. 6 (2022): EPRA International Journal of Multidisciplinary Research (IJMR)

Section

Articles