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dc.contributor.author
Ha-Neul Yeom
dc.contributor.author
Myunggwon Hwang
dc.contributor.author
Mi-Nyeong Hwang
dc.contributor.author
Hanmin Jung
dc.date.accessioned
2018-10-12T04:51:13Z
dc.date.available
2018-10-12T04:51:13Z
dc.date.issued
2014-09-30
dc.identifier.issn
2287-4577
dc.identifier.uri
https://repository.kisti.re.kr/handle/10580/8658
dc.description.abstract
In recent years, several studies have proposed making use of the Twitter micro-blogging service to track various trends in online media and discussion. In this study, we specifically examine the use of Twitter to track discussions of food safety in the Korean language. Given the irregularity of keyword use in most tweets, we focus on optimistic machine-learning and feature set selection to classify collected tweets. We build the classifier model using Naive Bayes & Naive Bayes Multinomial, Support Vector Machine, and Decision Tree Algorithms, all of which show good performance. To select an optimum feature set, we construct a basic feature set as a standard for performance comparison, so that further test feature sets can be evaluated. Experiments show that precision and F-measure performance are best when using a Naive Bayes Multinomial classifier model with a test feature set defined by extracting Substantive, Predicate, Modifier, and Interjection parts of speech.
dc.format
application/pdf
dc.language.iso
eng
dc.relation.ispartofseries
Journal of Information Science Theory and Practice
dc.title
Study of Machine-Learning Classifier and Feature Set Selection for Intent Classification of Korean Tweets about Food Safety
dc.type
Article
dc.rights.license
CC_BY
dc.identifier.doi
10.1633/JISTaP.2014.2.3.3
dc.citation.endPage
39
dc.citation.number
3
dc.citation.startPage
29
dc.citation.volume
2
dc.identifier.bibliographicCitation
vol. 2, no. 3, page. 29 - 39
dc.subject.keyword
Twitter
dc.subject.keyword
Tweets
dc.subject.keyword
Machine-learning Feature
dc.subject.keyword
Text Classification
dc.rights.holder
KISTI
Appears in Collections:
8. KISTI 간행물 > JISTaP > Vol. 2 - No. 3
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