Automatic Construction of Sentiment Lexicon by Analyzing SMS Bigdata

被引：0
|
作者：
Kang, Seung-Shik [1 ]
Lee, Minhaeng [2 ]
机构：
[1] Kookmin Univ, Dept Comp Sci, Seoul, South Korea
[2] Yonsei Univ, Dept German Language & Literature, Seoul, South Korea
来源：
2018 IEEE INTERNATIONAL CONFERENCE ON BIG DATA (BIG DATA) | 2018年
基金：
新加坡国家研究基金会;
关键词：
SMS bigdata; sentiment word; automatic dictionary construction; sentiment lexicon;
D O I：
暂无
中图分类号：
TP18 [人工智能理论];
学科分类号：
081104 ; 0812 ; 0835 ; 1405 ;
摘要：
SMS messages are expected to be different in vocabularies other than those used in ordinary written sentences. We explored an automatic construction of a sentiment lexicon through the extraction of sentiment words by analyzing SMS big data and general-purpose text corpus. Sentiment terms are extracted through POS tagging of the sentences, and the high frequency words in the corpus are compared. As a result, the use of adjectives is more frequent than that of verbs in SMS sentences, and the words of sentiment expression are frequently used. Using these characteristics, we proposed a method to automatically construct a sentiment word dictionary.
引用
收藏
页码：5348 / 5350
页数：3
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