Me Too Movement Sentiment Analysis
DM. Rama Bai1, Charishma Kuna2, J. Sreedevi3, G. Shantha4
1Dr M Rama Bai*, Professor, Department of Computer Science and Engineering, Mahatma Gandhi Institute of Technology, Hyderabad, India.
2Ms.Charishma Kuna, Student, Department of Computer Science and Engineering, Mahatma Gandhi Institute of Technology, Hyderabad, India.
3Mrs. J. Sreedevi, Assistant Professor, Department of Computer Science and Engineering, Mahatma Gandhi Institute of Technology, Hyderabad, India.
4Dr G. Shantha, Assistant Professor, Department of Mathematics & Humanities, Mahatma Gandhi Institute of Technology, Hyderabad, India.
Manuscript received on March 15, 2020. | Revised Manuscript received on March 30, 2020. | Manuscript published on April 10, 2020. | PP: 1065-1068 | Volume-9 Issue-6, April 2020. | Retrieval Number: F4167049620/2020©BEIESP | DOI: 10.35940/ijitee.F4167.049620
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© The Authors. Blue Eyes Intelligence Engineering and Sciences Publication (BEIESP). This is an open access article under the CC BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/)
Abstract: Sentiment Analysis (SA) is a current field of study in text mining. The subjectivity of text, sentiment, and opinions are treated computationally by SA. This study examines the sentiment of the tweets containing “#metoo”. As a comparison, the same analysis was performed on the MenToo movement. MeToo started picking up significance in India with the expanding ubiquity of the global development, and later gathered sharp force in October 2018 in the film business of Bollywood, focused in Mumbai, when Tanushree Dutta blamed Nana Patekar for lewd behavior. An Indian filmmaker has joined calls for the development of a “#MenToo” movement for men’s rights, saying it should be “as important as #MeToo. This case study gathers around 20,000 tweets from the major cities of India for the duration of a week. Tweets were analyzed through the ‘sentiments’ dataset of tidytext (afinn, bing, nrc) and RSentiments dataset. The goal was to understand the overall sentiment better and find the associated patterns. With the hashtag analysis, it can be seen that #metoo was associated with the film industry, whereas #mentoo was more rooted in the cause. The comparison of likes and retweets shows that the #metoo movement has over 70% more engagement than #mentoo.
Keywords: #Metoo, #Mentoo, Sentiment Analysis, R Sentiments, Tidy Text.
Scope of the Article: Predictive Analysis