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Title
Study of Machine-Learning Classifier and Feature Set Selection for Intent Classification of Korean Tweets about Food Safety
Author(s)
Ha-Neul YeomMyunggwon HwangMi-Nyeong HwangHanmin Jung
Publication Year
2014-09-30
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.
Keyword
Twitter; Tweets; Machine-learning Feature; Text Classification
Journal Title
Journal of Information Science Theory and Practice
Citation Volume
2
ISSN
2287-4577
DOI
10.1633/JISTaP.2014.2.3.3
Files in This Item:
Thumbnail E1JSCH_2014_v2n3_29.pdf178.21 kBDownload
Appears in Collections:
8. KISTI 간행물 > JISTaP > Vol. 2 - No. 3
Type
Article
URI
https://repository.kisti.re.kr/handle/10580/8658
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