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Year 2021, Volume: 5 Issue: 2, 173 - 182, 18.12.2021

Mustafa Özgür Cingiz Ece Çeliktaş

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

Abstract

Thanks

Makalemize katkılarınız ve değerlendirmeleriniz için teşekkür ederiz.

References

  • Barkur, G. and Vibha, G. B. K. (2020). Sentiment analysis of nationwide lockdown due to COVID 19 outbreak: Evidence from India. Asian journal of psychiatry, 51, 10208
  • Chakraborty, K., Bhatia, S., Bhattacharyya, S., Platos, J., Bag, R. and Hassanien, A. E. (2020). Sentiment Analysis of COVID-19 tweets by Deep Learning Classifiers—A study to show how popularity is affecting accuracy in social media. Applied Soft Computing, 97, 106754.
  • Alamoodi, A, et al. (2020). Sentiment analysis and its applications in fighting COVID-19 and infectious diseases: A systematic review. Expert systems with applications, 114155.
  • Rustam, F., Khalid, M., Aslam, W., Rupapara, V., Mehmood, A. and Choi, G. S. (2021). A performance comparison of supervised machine learning models for Covid-19 tweets sentiment analysis. Plos one, 16(2), e0245909.
  • [Ayan, B., Kuyumcu, B., Ciylan, B. (2019) Detection of Islamophobic Tweets on Twitter Using Sentiment Analysis, Gazi University Journal of Science Part C, 7(2), pp 495-502.
  • İlhan, N., Sağaltıcı D. (2020) Sentiment Analysis in Twitter, Harran University Journal of Engineering, 5(2), pp. 146-156, doi: 10.46578/humder.772929
  • Akın, B. ve Şimşek, T. (2018) Adaptive Learning Lexicon Based Sentiment Analysis Proposal, Information Technologies Journal, 11(3), doi: 10.17671/gazibtd.342419
  • Uslu, A., Tekin, S. ve Aytekin, T. (2019) Sentiment Analyasis In Turkish Film Comments, IEEE 27th Signal Processing and Communications (SIU), doi: 10.1109/SIU.2019.8806355
  • Neethu, M.S., Rajasree, R. (2013) Sentiment Analysis in Twitter Using Machine Learning Techniques, 2013 Fourth International Conference on Computing, Communications and Networking Technologies (ICCCNT), doi: 10.1109/ICCCNT.2013.6726818
  • Saha, S., Yadav, J. ve Ranjan, P. (2017) Proposed Approach for Sarcasm Detectionin Twitter, Indian JNournal of Science and Technology, 10(25), pp. 1-8.
  • Gautam, G., & Yadav, D. (2014). Sentiment analysis of twitter data using machine learning approaches and semantic analysis. 2014 Seventh International Conference on Contemporary Computing (IC3). doi:10.1109/ic3.2014.6897213
  • Wongkar, M., & Angdresey, A. (2019). Sentiment Analysis Using Naïve Bayes Algorithm of The Data Crawler: Twitter. 2019 Fourth International Conference on Informatics and Computing (ICIC). doi:10.1109/icic47613.2019.8985884
  • Mandloi, L., & Patel, R. (2020). Twitter Sentiments Analysis Using Machine Learning Methods. 2020 International Conference for Emerging Technology (INCET). doi:10.1109/incet49848.2020.9154183
  • El Rahman, S. A., AlOtaibi, F. A., & AlShehri, W. A. (2019). Sentiment Analysis of Twitter Data. 2019 International Conference on Computer and Information Sciences (ICCIS). doi:10.1109/iccisci.2019.8716464
  • Al Shammari, A. S. (2018). Real-time Twitter Sentiment Analysis using 3-way classifier. 2018 21st Saudi Computer Society National Computer Conference (NCC). doi:10.1109/ncg.2018.8593205
  • Documentation–tweepy, Tweepy. "3.5. 0 documentation." (2020).
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  • Pedregosa, Fabian, et al. (2011) Scikit-learn: Machine learning in Python. Journal of machine Learning research 12, 2825-2830.
  • Çelik, Ö, Osmanoğlu, U, Çanakçı, B. (2020). Sentiment Analysis from Social Media Comments, Mühendislik Bilimleri ve Tasarım Dergisi, 8 (2), 366-374. DOI: 10.21923/jesd.546224
  • Kaynar, O, Görmez, Y., Yıldız, M. ve Albayrak, A. (2016) Sentiment Analysis with Machine Learning Techniques, Processing Symposium (IDAP'16), pp. 17-18.
  • Çoban, Ö., Ozyer, B. ve Ozyer, G. (2015) Sentiment Analysis for Turkish Twitter Feeds, 3th Signal Processing and Communications Applications Conference (SIU), pp. 2388-2391.

Keyword-based Sentiment Analysis of Covid-19 Related Tweets

Year 2021, Volume: 5 Issue: 2, 173 - 182, 18.12.2021

Mustafa Özgür Cingiz Ece Çeliktaş

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

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

Social Networks Microblogs Sentiment Analysis Covid-19 Classification

References

  • Barkur, G. and Vibha, G. B. K. (2020). Sentiment analysis of nationwide lockdown due to COVID 19 outbreak: Evidence from India. Asian journal of psychiatry, 51, 10208
  • Chakraborty, K., Bhatia, S., Bhattacharyya, S., Platos, J., Bag, R. and Hassanien, A. E. (2020). Sentiment Analysis of COVID-19 tweets by Deep Learning Classifiers—A study to show how popularity is affecting accuracy in social media. Applied Soft Computing, 97, 106754.
  • Alamoodi, A, et al. (2020). Sentiment analysis and its applications in fighting COVID-19 and infectious diseases: A systematic review. Expert systems with applications, 114155.
  • Rustam, F., Khalid, M., Aslam, W., Rupapara, V., Mehmood, A. and Choi, G. S. (2021). A performance comparison of supervised machine learning models for Covid-19 tweets sentiment analysis. Plos one, 16(2), e0245909.
  • [Ayan, B., Kuyumcu, B., Ciylan, B. (2019) Detection of Islamophobic Tweets on Twitter Using Sentiment Analysis, Gazi University Journal of Science Part C, 7(2), pp 495-502.
  • İlhan, N., Sağaltıcı D. (2020) Sentiment Analysis in Twitter, Harran University Journal of Engineering, 5(2), pp. 146-156, doi: 10.46578/humder.772929
  • Akın, B. ve Şimşek, T. (2018) Adaptive Learning Lexicon Based Sentiment Analysis Proposal, Information Technologies Journal, 11(3), doi: 10.17671/gazibtd.342419
  • Uslu, A., Tekin, S. ve Aytekin, T. (2019) Sentiment Analyasis In Turkish Film Comments, IEEE 27th Signal Processing and Communications (SIU), doi: 10.1109/SIU.2019.8806355
  • Neethu, M.S., Rajasree, R. (2013) Sentiment Analysis in Twitter Using Machine Learning Techniques, 2013 Fourth International Conference on Computing, Communications and Networking Technologies (ICCCNT), doi: 10.1109/ICCCNT.2013.6726818
  • Saha, S., Yadav, J. ve Ranjan, P. (2017) Proposed Approach for Sarcasm Detectionin Twitter, Indian JNournal of Science and Technology, 10(25), pp. 1-8.
  • Gautam, G., & Yadav, D. (2014). Sentiment analysis of twitter data using machine learning approaches and semantic analysis. 2014 Seventh International Conference on Contemporary Computing (IC3). doi:10.1109/ic3.2014.6897213
  • Wongkar, M., & Angdresey, A. (2019). Sentiment Analysis Using Naïve Bayes Algorithm of The Data Crawler: Twitter. 2019 Fourth International Conference on Informatics and Computing (ICIC). doi:10.1109/icic47613.2019.8985884
  • Mandloi, L., & Patel, R. (2020). Twitter Sentiments Analysis Using Machine Learning Methods. 2020 International Conference for Emerging Technology (INCET). doi:10.1109/incet49848.2020.9154183
  • El Rahman, S. A., AlOtaibi, F. A., & AlShehri, W. A. (2019). Sentiment Analysis of Twitter Data. 2019 International Conference on Computer and Information Sciences (ICCIS). doi:10.1109/iccisci.2019.8716464
  • Al Shammari, A. S. (2018). Real-time Twitter Sentiment Analysis using 3-way classifier. 2018 21st Saudi Computer Society National Computer Conference (NCC). doi:10.1109/ncg.2018.8593205
  • Documentation–tweepy, Tweepy. "3.5. 0 documentation." (2020).
  • Loper, E., & Bird, S. (2002) NLTK: the natural language toolkit. arXiv preprint cs/0205028.
  • Loria, S., Keen, P., Honnibal, M., Yankovsky, R., Karesh, D., & Dempsey, E. (2014). Textblob: simplified text processing. Secondary TextBlob: simplified text processing, 3.
  • Pedregosa, Fabian, et al. (2011) Scikit-learn: Machine learning in Python. Journal of machine Learning research 12, 2825-2830.
  • Çelik, Ö, Osmanoğlu, U, Çanakçı, B. (2020). Sentiment Analysis from Social Media Comments, Mühendislik Bilimleri ve Tasarım Dergisi, 8 (2), 366-374. DOI: 10.21923/jesd.546224
  • Kaynar, O, Görmez, Y., Yıldız, M. ve Albayrak, A. (2016) Sentiment Analysis with Machine Learning Techniques, Processing Symposium (IDAP'16), pp. 17-18.
  • Çoban, Ö., Ozyer, B. ve Ozyer, G. (2015) Sentiment Analysis for Turkish Twitter Feeds, 3th Signal Processing and Communications Applications Conference (SIU), pp. 2388-2391.
There are 22 citations in total.

Details

Primary Language English
Subjects Engineering
Journal Section Research Articles
Authors

Mustafa Özgür Cingiz 0000-0003-4469-1440

Ece Çeliktaş 0000-0002-5619-6298

Publication Date December 18, 2021
Published in Issue Year 2021Volume: 5 Issue: 2

Cite

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 Cingiz MÖ, Çeliktaş E. Keyword-based Sentiment Analysis of Covid-19 Related Tweets. JISE. December 2021;5(2):173-182. doi:10.38088/jise.935963
Chicago Cingiz, Mustafa Özgür, and Ece Çeliktaş. “Keyword-Based Sentiment Analysis of Covid-19 Related Tweets”. Journal of Innovative Science and Engineering 5, no. 2 (December 2021): 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 M. Ö. Cingiz and E. Çeliktaş, “Keyword-based Sentiment Analysis of Covid-19 Related Tweets”, JISE, vol. 5, no. 2, pp. 173–182, 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 2021), 173-182. https://doi.org/10.38088/jise.935963.
JAMA 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, 2021, pp. 173-82, doi:10.38088/jise.935963.
Vancouver Cingiz MÖ, Çeliktaş E. Keyword-based Sentiment Analysis of Covid-19 Related Tweets. JISE. 2021;5(2):173-82.
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