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오피니언 마이닝 연구를 위한 정치기사 댓글의 ‘아니다’ 부정 의문문 분석
신동혁(Shin, Donghyok),남지순(Jeesun Nam) 언어과학회 2016 언어과학연구 Vol.0 No.78
This study analyzes the sentences where a negator anita ‘not be’ is not used to convert the opinion polarity of the main predicative elements when it appears in certain types of interrogatives in Korean political news reply texts. This study mainly provides the following findings: First, from morphological viewpoints, a typical interrogative suffix is used: -neunka takes about 54.4%. Second, from syntactic viewpoints, certain abstract nouns or bound nous such as kes without any postpositions occur most frequently with anita. Finally, from semantic viewpoints, the interrogatives based on anita express mostly a negative opinion, which suggests that this negator occurs with negative nouns and emphasizes that negative opinion. We suggested that anita occurring in these contexts is not used as a PSD (Polarity-Shifting Devices), but a rhetorical marker, which should be properly recognized in opinion mining.
한국어 감성 사전 DecoSelex 구축을 위한 영어 SentiWordNet 활용 및 보완 논의
신동혁(Shin, Donghyok),조동희(Cho, Donghee),남지순(Nam, Jeesun) 한국사전학회 2016 한국사전학 Vol.- No.28
In this study, we present the Korean Sentiment Lexicon DecoSelex that we constructed for the sentiment analysis of userᐨgenerated texts. This study starts with an examination of the results of Google translation of English SWN into Korean sentiment word lists. Among these Korean words, nonᐨtranslated words or irrelevant ones are observed, and therefore, after the manual elimination of these inappropriate terms, we obtained 3,665 candidates for Korean sentiment words. However, the nouns, being important in number in this list, are mostly medical disease names, and the adjectives, the most significant part of speech in general in the sentiment lexicon, appear extremely small in number. This situation led us to examine the DECO Korean electronic dictionary, a more sizable and reliable electronic resource for Korean. We obtained 35,452 sentimentᐨrelated words based on the DECO entries and classified them into several subᐨclasses according to their polarity properties and psychological meanings. For evaluating our sentiment lexicon DecoSelex, a dataset of 1,200 review texts of IT products was used. The current version of DecoSelex showed 83.6% precision & 83.2% recall for the positive words, and 72.9% precision & 69.1% recall for the negative words.