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Title: :  A NOVEL SIMILARITY APPROACH FOR ONLINE SENTIMENT TEXT
PaperId: :  10786
Published in:   International Journal Of Advance Research And Innovative Ideas In Education
Publisher:   IJARIIE
e-ISSN:   2395-4396
Volume/Issue:    Volume 5 Issue 4 2019
DUI:    16.0415/IJARIIE-10786
Licence: :   IJARIIE is licensed under a Creative Commons Attribution-ShareAlike 4.0 International License.

Author NameAuthor Institute
Zar Zar HninUniversity of Computer Studies (Mandalay)
Ei Ei MonUniversity of Computer Studies (Loikaw), Kayah State
Cho Cho KhaingUniversity of Computer Studies (Loikaw)

Abstract

Computer Engineering
social network, text similarity, semantic analysis, syntactic measures
The content and context of social network websites become crucial to know what the people are interested in and what kinds of information are spread among them depending on all commissions, comments, and actions need to analyze. Consequently, it becomes important that the brands listen carefully to what is said about their online business. Additionally, it demands more challenges to know whether the conversation leads to positive or negatives so that the impact of social network opinions can be measured to apply back in the real word problems. This paper finds the similar groups of social network activities, especially comments and posts of the users who shares about the same context depending on a specific topic. For this purpose, this paper introduces how to deal with finding the similarity between the contextual text of the users in semantic ways by filling the gap of syntactic measures in text similarity. Regarding the datasets, Twitter dataset, which is a popular dataset for sentiment analysis is used Respecting to the performance results, the proposed system achieves promising results with higher accuracy rate but lower error rate for both datasets available from online.

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IJARIIE Zar Zar Hnin, Ei Ei Mon, and Cho Cho Khaing. "A NOVEL SIMILARITY APPROACH FOR ONLINE SENTIMENT TEXT" International Journal Of Advance Research And Innovative Ideas In Education Volume 5 Issue 4 2019 Page 1569-1575
MLA Zar Zar Hnin, Ei Ei Mon, and Cho Cho Khaing. "A NOVEL SIMILARITY APPROACH FOR ONLINE SENTIMENT TEXT." International Journal Of Advance Research And Innovative Ideas In Education 5.4(2019) : 1569-1575.
APA Zar Zar Hnin, Ei Ei Mon, & Cho Cho Khaing. (2019). A NOVEL SIMILARITY APPROACH FOR ONLINE SENTIMENT TEXT. International Journal Of Advance Research And Innovative Ideas In Education, 5(4), 1569-1575.
Chicago Zar Zar Hnin, Ei Ei Mon, and Cho Cho Khaing. "A NOVEL SIMILARITY APPROACH FOR ONLINE SENTIMENT TEXT." International Journal Of Advance Research And Innovative Ideas In Education 5, no. 4 (2019) : 1569-1575.
Oxford Zar Zar Hnin, Ei Ei Mon, and Cho Cho Khaing. 'A NOVEL SIMILARITY APPROACH FOR ONLINE SENTIMENT TEXT', International Journal Of Advance Research And Innovative Ideas In Education, vol. 5, no. 4, 2019, p. 1569-1575. Available from IJARIIE, https://ijariie.com/AdminUploadPdf/A_NOVEL_SIMILARITY_APPROACH_FOR_ONLINE_SENTIMENT_TEXT_ijariie10786.pdf (Accessed : 22 March 2020).
Harvard Zar Zar Hnin, Ei Ei Mon, and Cho Cho Khaing. (2019) 'A NOVEL SIMILARITY APPROACH FOR ONLINE SENTIMENT TEXT', International Journal Of Advance Research And Innovative Ideas In Education, 5(4), pp. 1569-1575IJARIIE [Online]. Available at: https://ijariie.com/AdminUploadPdf/A_NOVEL_SIMILARITY_APPROACH_FOR_ONLINE_SENTIMENT_TEXT_ijariie10786.pdf (Accessed : 22 March 2020)
IEEE Zar Zar Hnin, Ei Ei Mon, and Cho Cho Khaing, "A NOVEL SIMILARITY APPROACH FOR ONLINE SENTIMENT TEXT," International Journal Of Advance Research And Innovative Ideas In Education, vol. 5, no. 4, pp. 1569-1575, Jul-Aug 2019. [Online]. Available: https://ijariie.com/AdminUploadPdf/A_NOVEL_SIMILARITY_APPROACH_FOR_ONLINE_SENTIMENT_TEXT_ijariie10786.pdf [Accessed : 22 March 2020].
Turabian Zar Zar Hnin, Ei Ei Mon, and Cho Cho Khaing. "A NOVEL SIMILARITY APPROACH FOR ONLINE SENTIMENT TEXT." International Journal Of Advance Research And Innovative Ideas In Education [Online]. volume 5 number 4 (22 March 2020).
Vancouver Zar Zar Hnin, Ei Ei Mon, and Cho Cho Khaing. A NOVEL SIMILARITY APPROACH FOR ONLINE SENTIMENT TEXT. International Journal Of Advance Research And Innovative Ideas In Education [Internet]. 2019 [Cited : 22 March 2020]; 5(4) : 1569-1575. Available from: https://ijariie.com/AdminUploadPdf/A_NOVEL_SIMILARITY_APPROACH_FOR_ONLINE_SENTIMENT_TEXT_ijariie10786.pdf
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