Popularity of University based on Sentiment Analysis in Social Network Media
Chiu-Lin Koh1, Su-Cheng Haw2, Lay-Ki Soon3
1Chiu-Lin Koh, Department of Computing and Informatics, Multimedia University, Cyberjaya, Malaysia.
2Su-Cheng Haw, Department of Computing and Informatics, Multimedia University, Cyberjaya, Malaysia.
3Lay-Ki Soon, Department of Computing and Informatics, Multimedia University, Cyberjaya, Malaysia.
Manuscript received on 25 September 2019 | Revised Manuscript received on 04 October 2019 | Manuscript Published on 22 October 2019 | PP: 25-30 | Volume-8 Issue-3S October 2019 | Retrieval Number: C10061083S19/2019©BEIESP | DOI: 10.35940/ijrte.C1006.1083S19
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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: Since the emergence of the social media, many studies are conducted on social media to gain information on social media users. Among these studies are sentiment analysis which is an analysis of user sentiments and emotions towards an object, term, or event based on what they post. Sentiment analysis are often conducted on sites like Facebook and Twitter because of their huge number of users and popularity. This paper aims to create a GUI-based sentiment analysis application to find out popularity of universities based on Twitter user’s sentiment. For this purpose, we firstly collected 600 tweets datasets, which is a mixture of 200 tweets each from Princeton University, Stanford University and University of Oxford for a period of 4 days (12/1/2018 to 15/1/2018). Second, the tweets were classified based on their sentiment into “positive”, “neutral” and “negative” tweets. Finally, the results were being analyzed in terms of Precision, Recall and F1 score. These information will help universities to gather information of public sentiment towards their institution and allow them to recognize their strength and weakness. Universities can use that information to improve their public image if needed in the future.
Keywords: Sentiment Analysis, Crawler, University Popularity, Social Network, Opinion Mining.
Scope of the Article: Social Network