A Proposed Sentiment Analysis Deep Learning Algorithm for Analyzing COVID-19 Tweets

Harleen Kaur, Shafqat Ul Ahsaan, Bhavya Alankar, Victor Chang

Research output: Contribution to journalArticlepeer-review

10 Citations (Scopus)
5 Downloads (Pure)

Abstract

With the rise in cases of COVID-19, a bizarre situation of pressure was mounted on each country to make arrangements to control the population and utilize the available resources appropriately. The swiftly rising of positive cases globally created panic, anxiety and depression among people. The effect of this deadly disease was found to be directly proportional to the physical and mental health of the population. As of 28 October 2020, more than 40 million people are tested positive and more than 1 million deaths have been recorded. The most dominant tool that disturbed human life during this time is social media. The tweets regarding COVID-19, whether it was a number of positive cases or deaths, induced a wave of fear and anxiety among people living in different parts of the world. Nobody can deny the truth that social media is everywhere and everybody is connected with it directly or indirectly. This offers an opportunity for researchers and data scientists to access the data for academic and research use. The social media data contains many data that relate to real-life events like COVID-19. In this paper, an analysis of Twitter data has been done through the R programming language. We have collected the Twitter data based on hashtag keywords, including COVID-19, coronavirus, deaths, new case, recovered. In this study, we have designed an algorithm called Hybrid Heterogeneous Support Vector Machine (H-SVM) and performed the sentiment classification and classified them positive, negative and neutral sentiment scores. We have also compared the performance of the proposed algorithm on certain parameters like precision, recall, F1 score and accuracy with Recurrent Neural Network (RNN) and Support Vector Machine (SVM).

Original languageEnglish
Pages (from-to)1417-1429
Number of pages13
JournalInformation Systems Frontiers
Volume23
Issue number6
DOIs
Publication statusPublished - 1 Dec 2021

Bibliographical note

Funding Information:
This research work is catalyzed and supported by the National Council for Science and Technology Communication (NCSTC), Department of Science and Technology, Ministry of Science and Technology (Govt. of India), New Delhi, India (Grant Recipient: Dr. Harleen Kaur). This work is partly supported by VC Research (VCR 000116) for Prof. Chang.

Publisher Copyright:
© 2021, The Author(s), under exclusive licence to Springer Science+Business Media, LLC, part of Springer Nature.

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