Keyword-based Sentiment Analysis of Covid-19 Related Tweets

Mustafa Özgür Cingiz; Ece Çeliktaş

doi:10.38088/jise.935963

EN

Keyword-based Sentiment Analysis of Covid-19 Related Tweets

Abstract

With the emergence of Web 2.0, internet users share their feelings, thoughts and ideas with other people using social networks. Understanding people's thought analysis is important for examining marketing and user feedback in social networks. For this reason, sentiment analysis on social networks with machine learning algorithms is a popular field of study. Our study is based on thesentiment analysis of people against the new coronavirus, which affects the world. People can have different moods due to pandemia. The governance of mental issues must be observed to manage the pandemic time period more successfully. In this article, we retrieved 387,953 tweets due to the ten most frequently used COVID-19 related keywords. The most frequently used keywords about COVID-19 which enable to obtain and assess the reaction of Twitter users are investigated. Even if COVID-19 is a health issue and tweets about COVID-19 is expected to contain negative content, we found positive, negative and neutral tweets to analyze texts using sentiment analysis and machine learning approaches. We applied four classifiers like logistic regression, multinomial naive Bayes, support vector machines and decision tree. These classifiers are well studied and utilized in many studies which we mentioned in our study. The performance of the support vector machine, decision tree and logistic regression classifiers are close to each other. The lowest F-score is obtained from multinominal naive Bayes classifier. The classification results for each negative, neutral and positive class were compared separately in our study.

Keywords

Thanks

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Details

Primary Language

English

Subjects

Engineering

Journal Section

Research Article

Authors

Mustafa Özgür Cingiz ^*
0000-0003-4469-1440
Türkiye

Ece Çeliktaş
0000-0002-5619-6298
Türkiye

Publication Date

December 18, 2021

Submission Date

May 11, 2021

Acceptance Date

October 9, 2021

Published in Issue

Year 2021 Volume: 5 Number: 2

DOI

https://doi.org/10.38088/jise.935963

IZ

https://izlik.org/JA46SL25MB

Cite

RIS / Bibtex

APA

Cingiz, M. Ö., & Çeliktaş, E. (2021). Keyword-based Sentiment Analysis of Covid-19 Related Tweets. Journal of Innovative Science and Engineering, 5(2), 173-182. https://doi.org/10.38088/jise.935963

AMA

1.Cingiz MÖ, Çeliktaş E. Keyword-based Sentiment Analysis of Covid-19 Related Tweets. JISE. 2021;5(2):173-182. doi:10.38088/jise.935963

Chicago

Cingiz, Mustafa Özgür, and Ece Çeliktaş. 2021. “Keyword-Based Sentiment Analysis of Covid-19 Related Tweets”. Journal of Innovative Science and Engineering 5 (2): 173-82. https://doi.org/10.38088/jise.935963.

EndNote

Cingiz MÖ, Çeliktaş E (December 1, 2021) Keyword-based Sentiment Analysis of Covid-19 Related Tweets. Journal of Innovative Science and Engineering 5 2 173–182.

IEEE

[1]M. Ö. Cingiz and E. Çeliktaş, “Keyword-based Sentiment Analysis of Covid-19 Related Tweets”, JISE, vol. 5, no. 2, pp. 173–182, Dec. 2021, doi: 10.38088/jise.935963.

ISNAD

Cingiz, Mustafa Özgür - Çeliktaş, Ece. “Keyword-Based Sentiment Analysis of Covid-19 Related Tweets”. Journal of Innovative Science and Engineering 5/2 (December 1, 2021): 173-182. https://doi.org/10.38088/jise.935963.

JAMA

1.Cingiz MÖ, Çeliktaş E. Keyword-based Sentiment Analysis of Covid-19 Related Tweets. JISE. 2021;5:173–182.

MLA

Cingiz, Mustafa Özgür, and Ece Çeliktaş. “Keyword-Based Sentiment Analysis of Covid-19 Related Tweets”. Journal of Innovative Science and Engineering, vol. 5, no. 2, Dec. 2021, pp. 173-82, doi:10.38088/jise.935963.

Vancouver

1.Mustafa Özgür Cingiz, Ece Çeliktaş. Keyword-based Sentiment Analysis of Covid-19 Related Tweets. JISE. 2021 Dec. 1;5(2):173-82. doi:10.38088/jise.935963

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https://doi.org/10.29109/gujsc.1588839