Authors : Irfan Qutab; Muhammad Aqeel; Unaiza Fatima; Imtiaz Ahmed

Volume/Issue : Volume 9 - 2024, Issue 8 - August

Google Scholar : https://tinyurl.com/yc883pbf

Scribd : https://tinyurl.com/mr47mhp8

DOI : https://doi.org/10.38124/ijisrt/IJISRT24AUG640

Abstract : The COVID-19 pandemic has brought about a surge in online discussions and social media activity, making it crucial to analyze public sentiment towards the virus and related topics. This thesis focuses on Sentiment Analysis of COVID-19 data on Twitter, employing Multinomial Logistic Regression as the primary classification algorithm. This research explores Sentiment Analysis of COVID-19 data on Twitter using Multinomial Logistic Regression. It constructs a tweet dataset reflecting various sentiments—positive, negative, and neutral. The data undergoes preprocessing, and a Sentiment Analysis model is built, with 70% of data for training and 30% for testing. The model uses Count-Vectorizer, Tf-idf for feature extraction, and Multinomial Logistic Regression to classify tweets. The study achieves state-of-the-art results with a high accuracy of 95.14%, demonstrating the effectiveness of this approach. The results offer valuable insights into public sentiment during crises, aiding in decision-making and communication strategies.

Keywords : Sentiment Analysis, COVID-19, Twitter, Multinomial Logistic Regression, Social Media.

References :

1. E. Doǧan and B. Kaya, "Deep learning based Sentiment Analysisand text summarization in social networks," International Artificial Intelligence and Data Processing Symposium (IDAP),IEEE., pp. 1-6, 2019 .
2. W. P. Ramadhan, S. A. Novianty and S. C. Setianingsih, "Sentiment Analysisusing multinomial logistic regression," International Conference on Control, Electronics, Renewable Energy and Communications (ICCREC),IEEE, pp. 46-49, 2017.
3. Agarwal, B. Xie, I. Vovsha, O. Rambow and R. J. Passonneau, "Sentiment Analysisof twitter data," In Proceedings of the workshop on language in social media, pp. 30-38, 2011.
4. Tyagi and N. Sharma, "Sentiment Analysisusing logistic regression and effective word score heuristic," International Journal of Engineering and Technology (UAE), vol. 7(2.24), pp. 20-23, 2018.
5. D. Wang, B. Hu, C. Hu, F. Zhu, X. Liu, J. Zhang and Z. Peng, "Clinical characteristics of 138 hospitalized patients with 2019 novel coronavirus–infected pneumonia in Wuhan, China," vol. 323(11), pp. 1061-1069, 2020.
6. Y. Y. Zheng, Y. T. Ma, J. Y. Zhang and X. Xie, "COVID-19 and the cardiovascular system," Nature reviews cardiology, vol. 17(5), pp. 259-260, 2020.
7. W. Medhat, A. Hassan and H. Korashy, "Sentiment Analysisalgorithms and applications: A survey," Ain Shams engineering journal, vol. 5(4), pp. 1093-1113, 2014.
8. K. Chakraborty, S. Das and A. K. Kolya, "Sentiment Analysisof covid-19 tweets using evolutionary classification-based LSTM model.," In Proceedings of Research and Applications in Artificial Intelligence, no. Springer, Singapore, pp. 75-86, 2021.
9. Kruspe, M. Häberle, I. Kuhn and X. X. Zhu, "Cross-language Sentiment Analysisof european twitter messages duringthe covid-19 pandemic," arXiv:2008, p. 12172, 2020.
10. Wilson, T., Wiebe, J., & Hoffmann, P. (2005, october). Recognizing contextual polarity in phrase-level sentiment analysis. In Proceedings of human language technology conference and conference on empirical methods in natural language processing, 347-354.
11. Agarwal, A., Xie, B., Vovsha, I., Rambow, O., & Passonneau, R. J. (2011, June). Sentiment analysis of twitter data. In Proceedings of the workshop on language in social media (LSM 2011), 30-38.
12. Xu, T., Peng, Q., & Cheng, Y. (2012). Identifying the semantic orientation of terms using S-HAL for sentiment analysis. Knowledge-Based Systems, 35, 279-289.
13. Yu, L. C., Wu, J. L., Chang, P. C., & Chu, H. S. (2013). Using a contextual entropy model to expand emotion words and their intensity for the sentiment classification of stock market news. Knowledge-Based Systems, 41, 89-97.
14. Maks, I., & Vossen, P. (2012). A lexicon model for deep sentiment analysis and opinion mining applications. Decision support systems, 53(4), 680-688.
15. Qutab, I., Malik, K. I., & Arooj, H. (2020). Sentiment Analysis for Roman Urdu Text over Social Media, a Comparative Study. arXiv preprint arXiv:2010.16408.
16. Qutab, I., Malik, K. I., & Arooj, H. (2022). Sentiment Classification Using Multinomial Logistic Regression on Roman Urdu Text. International Journal of Innovations in Science & Technology, 4(2), 223-335.
17. T. Sahni, C. Chandak, N. R. Chedeti and M. Singh, "Efficient Twitter Sentiment Analysisusing subjective distant supervision," International Conference on Communication Systems and Networks (COMSNETS), IEEE, pp. 548-553, 2017.
18. Croft, W. B., Metzler, D., & Strohman, T. (2010). Search engines: Information retrieval in practice (Vol. 520). Reading: Addison-Wesley.
19. Vruniotis, V. (2017). Machine Learning Tutorial: The Multinomial Logistic Regression (Softmax Regression)| Datumbox. Blog. datumbox. com. Np.