An Unsupervised Clustering Approach for Twitter Sentimental Analysis: A Case Study for George Floyd Incident

© 2020 by IJCTT Journal
Volume-68 Issue-6
Year of Publication : 2020
Authors : Balaji Karumanchi
DOI :  10.14445/22312803/IJCTT-V68I6P108

How to Cite?

Balaji Karumanchi, "An Unsupervised Clustering Approach for Twitter Sentimental Analysis: A Case Study for George Floyd Incident," International Journal of Computer Trends and Technology, vol. 68, no. 6, pp. 46-50, 2020. Crossref,

Performing sentiment analysis is vital which can be used to find out the public review about a product or ongoing events in the world. Public can easily and efficiently express their perspectives and ideas on a wide variety of topics like events, services and brands via social networking websites. Social networks especially Twitter is continuously updated with public views, expressions and opinions. In this we have performed twitter sentimental analysis to review public opinion about George Floyd incident using Twitter data. Text mining and sentimental analysis are used Text mining and sentiment analysis to analyse unstructured tweet text to extract positive and negative polarity about this incident. Moreover, tweet frequency analysis has been done to view trend in public opinion across 9 days’ tweet text data. We found out that majority of the people have attitude towards this incident by using 3 hashtags and overall data.

George Floyd, twitter sentimental analysis, K-Means clustering, data mining

[1] J. Clement, "Global digital population as of April 2020,", 2020, [Online]. Available:, Accessed on: Jun 10, 2020.
[2] E. Cambria, B. Schuller, Y. Xia, and C. Havasi, "New avenues in opinion mining and sentiment analysis," IEEE Intelligent systems, vol. 28, no. 2, pp. 15-21, 2013.
[3] B. Liu, "Sentiment analysis and opinion mining," Synthesis lectures on human language technologies, vol. 5, no. 1, pp. 1-167, 2012.
[4] N. Majumder et al., "Improving Aspect-Level Sentiment Analysis with Aspect Extraction," arXiv preprint arXiv:2005.06607, 2020.
[5] B. Pang and L. Lee, "Opinion Mining and Sentiment Analysis. Foundations and Trends in Information Retrieval," 2008.
[6] J. A. Rathod, S. Vignesh, and A. J. Shetty, "Sentiment Analysis of Smartphone Product Reviews Using Weightage Calculation," in Advances in Computing and Intelligent Systems: Springer, 2020, pp. 427-437.
[7] A. Ritter, S. Clark, and O. Etzioni, "Named entity recognition in tweets: an experimental study," in Proceedings of the conference on empirical methods in natural language processing, 2011: Association for Computational Linguistics, pp. 1524-1534.
[8] H. Becker, M. Naaman, and L. Gravano, "Beyond trending topics: Real-world event identification on twitter," in Fifth international AAAI conference on weblogs and social media, 2011.
[9] Malladihalli S Bhuvan , Vinay D Rao , Siddharth Jain , T S Ashwin , and R. M. R. Guddeti, "Semantic sentiment analysis using context specific grammar," presented at the International Conference On Computing, Communication and Automation., 2015.
[10] C. C. Chen and Y.-D. Tseng, "Quality evaluation of product reviews using an information quality framework," Decision Support Systems, vol. 50, no. 4, pp. 755-768, 2011.
[11] M. Gayathri, S. S. Nisha, and M. M. Sathik, "Twitter Sentiment Analysis using Naive Bayes Classification," Studies in Indian Place Names, vol. 40, no. 71, pp. 1473-1478, 2020.
[12] H. Suresh, "An unsupervised fuzzy clustering method for twitter sentiment analysis," in 2016 International Conference on Computation System and Information Technology for Sustainable Solutions (CSITSS), 2016: IEEE, pp. 80-85.
[13] T. Vaseeharan and A. Aponso, "Review On Sentiment Analysis of Twitter Posts About News Headlines Using Machine Learning Approaches and Naïve Bayes Classifier," in Proceedings of the 2020 12th International Conference on Computer and Automation Engineering, 2020, pp. 33-37.
[14] W. Ahmed, J. Vidal-Alaball, J. Downing, and F. L. Seguí, "COVID-19 and the 5G conspiracy theory: social network analysis of Twitter data," Journal of Medical Internet Research, vol. 22, no. 5, p. e19458, 2020.
[15] Y. Chandra and A. Jana, "Sentiment Analysis using Machine Learning and Deep Learning," in 2020 7th International Conference on Computing for Sustainable Global Development (INDIACom), 2020: IEEE, pp. 1-4.
[16] J. MacQueen, "Some methods for classification and sanalysis of multivariate observations," in Proceedings of the fifth Berkeley symposium on mathematical statistics and probability, 1967, vol. 1, no. 14: Oakland, CA, USA, pp. 281-297.
[17] B.Srinivasa Rao, S.Vellusamy Raddy, "A Hard K-Means Clustering Techniques for Information Retrieval from Search Engine" SSRG International Journal of Computer Science and Engineering 4.2 (2017)