Deep Sentiment Classification using Topic Modeling for Covid-19

P Velvizhy; K Arul Deepa; E Shanmuga Priya

doi:10.17485/IJST/v16i36.1464

Article

Deep Sentiment Classification using Topic Modeling for Covid-19

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Indian Journal of Science and Technology

DOI: 10.17485/IJST/v16i36.1464

Year: 2023, Volume: 16, Issue: 36, Pages: 2929-2937

Original Article

Deep Sentiment Classification using Topic Modeling for Covid-19

P Velvizhy^1*, K Arul Deepa², E Shanmuga Priya¹

¹Department of Computer Science and Engineering, College of Engineering Guindy, Anna University, Chennai, Tamil Nadu, India
²Department of Information Science and Technology, College of Engineering Guindy, Chennai, Tamil Nadu, India

*Corresponding Author
Email: [email protected]

Received Date:26 June 2023, Accepted Date:17 August 2023, Published Date:27 September 2023

This work is licensed under a Creative Commons Attribution 4.0 International License.

Abstract

Objectives: To extract various topics related to Covid-19 from Twitter API using LDA topic modelling technique and to analyse the sentiment of the people about the extracted topics. An interactive Q/A system with both voice and text interface is also proposed to guide COVID-19 related decision-making. And also to summarize the tweets containing a query and to suggest suitable solutions. Method: The proposed extracts Covid-19 related tweets from twitter API and uses Natural Language Process (NLP) method based on topic modeling to uncover various issues related to COVID-19 from public opinions. The training dataset consists of 3,38,666 COVID 19 related comments and the testing dataset consists of 1,12,888 comments. LSTM recurrent neural network is used for sentiment analysis of the extracted tweets and to produce summary for each topic identified through topic modelling. Findings: The accuracy comparison has been done for the existing system against the proposed model with respect various machine learning classifiers. The findings are- LSTM gives an accuracy of 79.5%, the Naïve Bayes classifier gives the accuracy of 74%, the Multinomial Naïve Bayes gives an accuracy of 74.5%, whereas the linear regression classifier achieves an accuracy of 76%, KNN classifier achieves an accuracy of 74.5% and the random forest with an accuracy of 75.5%. Novelty: The proposal of interactive Question Answering system is first of its kind. This work sheds light on the importance of using public opinions and suitable computational techniques to understand issues surrounding Covid 19 and to guide related decision-making.

Keywords: COVID19; LDA Topic Modeling; LSTM; Sentiment Analysis; Q/A System

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Copyright

© 2023 Velvizhy et al. This is an open-access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited. Published By Indian Society for Education and Environment (iSee)