인문학
사회과학
자연과학
공학
의약학
농수해양학
예술체육학
복합학
DBpia AI
AI 에이전트
AI 아이디어
AI 리더
AI 뷰어
Citeasy
크롬 서지관리 다운로드
개인구독
소속 기관이 없으신 경우, 개인 정기구독을 하시면 저렴하게
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지원사업
학술연구/단체지원/교육 등 연구자 활동을 지속하도록 DBpia가 지원하고 있어요.
커뮤니티
연구자들이 자신의 연구와 전문성을 널리 알리고, 새로운 협력의 기회를 만들 수 있는 네트워킹 공간이에요.
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초록·키워드
The rapid development of information communications technology, especially Internet and smartphones, helps customers be more flexible and easier to access social networking sites and use them as effective communication tools. A huge number of informal messages are posted every day in social networking sites including comments, opinions and feedbacks about products, services or companies. These text data are not only in English but also in several other languages as the social networking sites develop across countries. It has become difficult and time consuming for individuals or organizations to effectively process the information underlined in these text data. Thanks to the development of opinion mining techniques, social media text data can be mined to explore customer opinions about products, services as well as information about competitors. This paper proposed models for opinion mining on Vietnamese social media text data. We collected social media text data from Facebook in Vietnam and designed a non-standard Vietnamese words dictionary to process informal Vietnamese text messages. We compared predictive performance of several opinion mining models in lexicon-based and machine learning approach and then proposed a hybrid model that combines the two approaches. The results show that using non-standard Vietnamese words dictionary improves predictive performance of opinion mining models, and hybrid models of lexicon-based and machine learning approach have better performance than single models. Based on this research outcomes, we provided recommendations in designing opinion mining models on non-English social media text data.
본문·목차
인공지능 문자 인식 모델을 통해 추출된 텍스트로, 일부 오타나 오류가 포함될 수 있으나 지속적으로 개선 중입니다.
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최근 본 자료 전체보기
UCI(KEPA) : I410-ECN-0101-2016-326-002902416
