An Unsupervised Clustering Approach for Twitter Sentimental Analysis: A Case Study for George Floyd Incident
|© 2020 by IJCTT Journal|
|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, https://doi.org/10.14445/22312803/IJCTT-V68I6P108
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
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