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
논문을 무제한 열람 이용할 수 있어요.
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.
논문 기본 정보
- 자료유형
- 학술저널
- 저자정보
- 발행연도
- 2017.10
- 수록면
- 330 - 340 (11page)
- DOI
- 10.7232/JKIIE.2017.43.5.330
이용수
초록· 키워드
Sentiment analysis plays an important role in both public and private sectors to understand consumers’ responses to products or voters’ reactions to policies. One of the most key success factors of sentiment analysis is to build an appropriate sentiment word dictionary. Many current existing approaches either heavily rely on the knowledge of domain experts or word co-occurrence statistics, the first of which causes low efficiency and high expenditure while the second of which suffers from incomplete data. In order to resolve these shortcomings, we propose a new domain-specific Korean word sentiment score evaluation method based on word embedding and graph based semi-supervised learning. First, words are embedded in a lower dimensional space by Word2Vec technique. Then, the word relation graph is constructed based on the similarity between words in the embedding space. Then, we assign sentiments to approximately 1% words utilizing some indicators like centrality measure. The sentiment scores of the other unlabeled words are automatically assigned by label propagation with semisupervised learning. To verify our proposed method, we collect 1.98 million review comments from three movie review websites. Experimental results show that the proposed method achieves about 93% accuracy of polarity classification.
#Sentiment Analysis
#Word Embedding
#Graph-Based Semi-Supervised Learning
#Label Propagation
#Lasso Regression
#Movie Review Evaluation
상세정보 수정요청해당 페이지 내 제목·저자·목차·페이지정보가 잘못된 경우 알려주세요!
목차
- 1. 서론
- 2. 선행연구
- 3. 방법론
- 4. 실험설계
- 5. 실험 결과
- 6. 결론 및 활용방안
- 참고문헌
참고문헌
참고문헌 신청최근 본 자료
UCI(KEPA) : I410-ECN-0101-2018-530-001328325
