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  1. University of Computer Studies, Yangon
  2. Conferences

Semi-supervised Domain Specified Event Extraction from Social Media

http://hdl.handle.net/20.500.12678/0000004736
http://hdl.handle.net/20.500.12678/0000004736
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414fb0ac-3a03-44bc-a136-011de2c6655a
Publication type
Article
Upload type
Publication
Title
Title Semi-supervised Domain Specified Event Extraction from Social Media
Language en
Publication date 2018-02-22
Authors
Nwe, San San
Kham, Nang Saing Moon
Description
Social media has quickly become popular as an important means that people, organizations use to spread information of divert events for various purposes, ranging from business intelligence to nation security. However, the language used in Twitter is heavily informal, ungrammatical, short and dynamic. Automatically detecting and categorizing events using streamed data is a difficult task, due to the presence of noise and irrelevant information. Therefore, as an emerging research area, event analysis from social media, Twitter has attracted much attention since 2010 and there are many attempts to detect and categorize events from social media. This paper proposes a framework to identify the events from twitter in a semi-supervised manner for targeted domain in specific location with SVM in combination with the corpus. The experimental results show that the semi-supervised SVM model outperforms a strong state-of-the-art semi-supervised classification model of Logic Regression, Navebays and Decision Tree.
Keywords
Social Media, twitter, Semi-supervised, Events, SVM
Identifier http://onlineresource.ucsy.edu.mm/handle/123456789/456
Journal articles
Sixteenth International Conferences on Computer Applications(ICCA 2018)
Conference papers
Books/reports/chapters
Thesis/dissertations
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