№1, 2022

Makrufa Sh.Hajirahimova, Marziya I. Ismayilova

The paper analyzes the views of Twitter users on the COVID-19 corona virus pandemic based on machine learning algorithms. The role of sentiment analysis increased with the advent of the social network era and the rapid spread of microblogging applications and forums. Social networks are the main sources for gathering information about users’ thoughts on various themes. People spend more time on social media to share their thoughts with others. One of the themes discussed on social networking platforms Twitter is the COVID-19 corona virus pandemic. In the paper, machine learning methods as Naive Bayes (NB), Support Vector Machine (SVM), Random Forest (RF), Neural Network (NN) are used to analyze the emotional “color” (positive, negative, and neutral) of tweets related to the COVID-19 corona virus pandemic. The experiments are conducted in Python programming using the scikit-learn library. A tweet database related to the COVID-19 corona virus pandemic from the Kaggle website is used for experiments. The RF classifier shows the highest performance in the experiments (pp.52-60).

Keywords:Sentiment analysis, Twitter, Mikroblogging, Machine learning, Naive Bayes, Neural Network
  • Liu, B. (2012). Sentiment analysis and opinion mining. Toronto: Morgan & Claypool Publishers.
  • Hajirahimova, M.Sh., Ismayilova, M.I. (2020). Sentiment analysis: problems and solutions. Problems of Information technology, 2, 111-123. (in Azerbaijani)
  • Evirgen, E. (2016). Sentiment analysis of turkish tweets. Master Thesis. Turkey: Bahcesehir University. https://www.academia.edu/38504379/IJMET_10_01_094_pdf?from=cover_page
  • Alguliev, R., Aliguliyev, R., Hajirahimova, M. (2010). Multi-document summarization model based on integer linear programming. Intelligent Control and Automation, 1(2), 105-111.
  • Kaur, C., Sharma, A. (2020). Twitter Sentiment Analysis on Coronavirus using Textblob. EasyChair Preprint, 2974, 1-10. https://easychair.org/publications/preprint/Fd5m
  • Dashtian, H., Murthy, D. (2021). CML-COVID: A large-scale covid-19 Twitter dataset with latent topics, sentiment and location information, Academia Letters, 1-9.
  • Samina, A. et al. (2021). Machine Learning Approach for COVID-19 Detection on Twitter. Computers, Materials & Continua, 68(2), 2231-2247.
  • Shofiya, C., Samina, A. (2021). Sentiment Analysis on COVID-19-Related Social Distancing in Canada Using Twitter Data. International Journal of Enviromental Research and Public Health, 18(11), 1-10.
  • Abdulaziz, M. et al. (2021). Topic based Sentiment Analysis for COVID-19 Tweets. International Journal of Advanced Computer Science and Applications, 12 (1), 626-636.
  • Vohra, S. M., Teraiya, J. B. (2013). A comparative study of sentiment Analysis techniques. İnternational Journal of Information, knowledge and research in Computer engineering, 2(2), 313-317.
  • Kumar, A., Sebastian, T. M. (2012). Sentiment Analysis on Twitter. International Journal of Computer Science, 9(4), 372-378. http://www.ijcsi.org/
  • Kolchyna, O. et al. (2015). Twitter Sentiment Analysis: Lexicon Method, MachineLearning Method and Their Combination, Cornell university, 1-32. arXiv.org
  • Borele, P., Borikar, D.A. (2016). An Approach to Sentiment Analysis using Artificial Neural Network with Comparative Analysis of Different Techniques. Journal of Computer Engineering (IOSR-JCE), 18(2), 64-69.
  • Spencer, J., Uchyigit, G. (2012). Sentimentor: Sentiment Analysis of Twitter Data. In The 1st International Workshop on Sentiment Discovery from Affective Data (SDAD), Bristol, UK, 28 September (pp.56-66).
  • Hasan, A., Moin, S., Karim, A., Shamshirband, S. (2018). Machine Learning-Based Sentiment Analysis for Twitter Accounts. International Journal of Mathematical and Computational Applications, 23(11), 1-15.
  • Yadav, S.J., Ranjan, P. (2017). Proposed Approach for Sarcasm Detection in Twitter Shubhodip. Indian Journal of Science and Technology, 10(25), 1-8.
  • Bahrainian, S.A., Denge, A. (2013). Sentiment Analysis using Sentiment Features. Proc. of IEEE/WIC/ACM International Conferences on Web Intelligence (WI) and Intelligent Agent Technology (IAT), Washington, USA, November 2013, (pp. 26-29).
  • Kawade, D.R., Oza, K.S. (2017). Sentiment Analysis: Machine Learning Approach. International Journal of Engineering and Technology, 9(3), 2183-2186.
  • Hafez, A.A., Xu, Y., Tjondronegoro, D. (2012). Product Reputation Model: An Opinion Mining Based Approach. Proc. of the 1st International Workshop on Sentiment Discovery from Affective Data, London, UK, January 2012 (pp.16-27). http://ceur-ws.org/Vol-917/
  • Saleena, A.N. (2018). An Ensemble Classification System for Twitter Sentiment Analysis. Proc. of the International Conference on Computational Intelligence and Data Science (ICCIDS), Gurugram, İndia, 937-946.
  • Sumathy, Dr.P., Muthukumari, S.M. (2018). Sentiment Analysis of Twitter Data Using Multi Class Semantic Approach. International Journal of Scientific Research in Computer Science, Engineering and Information Technology (IJSRCSEIT), 3(6), 262-269.
  • Shehu, H.A., Tokat, S., Sharif, Md. H., Uyaver, S. (2019). Sentiment Analysis of Turkish Twitter Data. Proc of the AIP Conference, 2183(1), AIP Publishing (pp. 1-4). https://doi.org/10.1063/1.5136197
  • Hajirahimova, M., Imamverdiyeva, A. (2018) Sentiment analysis of Twitter data.The 4 th Republican Conference on “Actual multidisciplinary scientific-practical problems of information security”, (in Azerbaijani) Baku, Azerbaijan, December 14, 2018 (pp. 245-248).   https://doi.org/10.25045/NCInfoSec.2018.59
  • Kumar, R. (2015). A survey on opinion mining and sentiment analysis: tasks, approaches and applications. Knowledge-Based Systems, 89, 14-46.
  • Kharde, V.A., Sonawane, S.S. Sentiment Analysis of Twitter Data: A (2016). Survey of Techniques Vishal A. International Journal of Computer Applications, 139(11), 5-15.  https://doi.org/5120/ijca2016908625
  • Tang, H., Tan, S., Cheng, X. (2009). A survey on sentiment detection of reviews. International Journal Expert Systems with Applications, 36(7), 10760–10773.
  • Medhat, W., Hassan, A., Korashy, H. (2014). Sentiment analysis algorithms and applications: A survey. Ain Shams Engineering Journal, 5(4), 1093–1113.
  • Vinodhini, G., Chandrasekaran, R.M. (2012). Sentiment Analysis and Opinion Mining: A Survey. International Journal of Advanced Research in Computer Science and Software Engineering, 2(6), 282-292.
  • Wawre, S.V., Deshmukh, S.N. (2016). Sentiment Classification using Machine Learning Techniques. International Journal of Science and Research, 5(4), 819-821.
  • Padmaja, S., Fatima, S.S. (2013). Opinion Mining and Sentiment Analysis –An Assessment of Peoples’ Belief: A Survey. International Journal of Ad hoc, Sensor & Ubiquitous Computing (IJASUC), 4(1), 21-33. http://www.ijsr.net
  • Pranali, B., Dilipkumar, A.B. (2016). An Approach to Sentiment Analysis using Artificial Neural Network with Comparative Analysis of Different Techniques. Journal of Computer Engineering, 18(2), 64-69.
  • Chena, L-S, Liub, C-H, Chiu, H-J. (2011). A neural network based approach for sentiment classification in the blogosphere. Journal of Informetrics, 5(2), 313–322.
  • Mathur, R., Bandil, D., Pathak, V. (2018). Analyzing Sentiment of Twitter Data using Machine Learning Algorithm. GADL Journal of Inventions in Computer Science and Communication Technology (JICSCT), 4(2), 1-7.