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- 학술저널
- 대한산업공학회
- 대한산업공학회지
- 대한산업공학회지 제43권 제5호
- 2017.10
- 330 - 340 (11 pages)
- KCI우수등재
DOI : 10.7232/JKIIE.2017.43.5.330
UCI(KEPA) : I410-ECN-0101-2018-530-001328325
초록
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.
목차
1. 서론
2. 선행연구
3. 방법론
4. 실험설계
5. 실험 결과
6. 결론 및 활용방안
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