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Classification Technique for Filtering Sentiment Vocabularies for the Enhancement of Accuracy of Opinion Mining
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- 발행기관
- 보안공학연구지원센터(IJUNESST) 바로가기
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- 간행물
- International Journal of u- and e- Service, Science and Technology 바로가기
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- 통권
- Vol.8 No.10 (2015.10)바로가기
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- 페이지
- pp.11-20
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- 저자
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- 언어
- 영어(ENG)
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- URL
- https://www.earticle.net/Article/A257323
※ 원문제공기관과의 협약기간이 종료되어 열람이 제한될 수 있습니다.
원문정보
초록
- 영어
- This thesis, as part of the creation of a text-mining-based sentiment dictionary to be applied in the Korean grammar structure, solves the problem of the enhancement of accuracy of opinion mining data by applying the filtering model of candidate sentiment vocabularies. The fact that the reliability of sensitive vocabularies shows huge variances according to the filtering modeling method applied has become a decreasing factor for the accuracy of the vocabularies in the opinion mining process, which is attributable to the fact there isn’t a success factor in the filtering modeling standard for precise selection of vocabularies. In this thesis, a filtering model of positive and negative vocabularies on candidate Korean sentiment vocabularies and a reliability scale for accuracy were suggested to solve such problems by applying the semi-structured data filtering model for the selection of candidate sentiment vocabularies of the Korean grammar. The study has confirmed through relevant performance assessment when filtering were applied in relation to 30%, 50% and 60% respectively with regard to candidate sentiment vocabularies upon collecting vocabularies obtained via sentence segmentation and classification into positive and negative vocabularies that exceptional accuracy of the opinion sentiment dictionary was shown via the 60% filtering.
목차
Abstract
1. Introduction
2. Related Work
2.1 Filtering of the Candidate Sentiment Vocabularies Set
2.2 Methodology on Data Filtering
3. Proposed Method
3.1 Source Data Type
3.2 Morpheme Analysis of the Sentence
3.3 Filtering of Tokenized Vocabularies in the Document
3.4 Data Feature Extraction
3.5 Execution Process of the Vocabulary Classification Model
3.6 Data Filtering
3.7 Opinion System
4. Performance Evaluation
5. Conclusion
Acknowledgement
References
키워드
Filtering Text Mining Opinion Mining Prediction Classification저자
간행물 정보
발행기관
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- 발행기관명
- 보안공학연구지원센터(IJUNESST) [Science & Engineering Research Support Center, Republic of Korea(IJUNESST)]
- 설립연도
- 2006
- 분야
- 공학>컴퓨터학
- 소개
- 1. 보안공학에 대한 각종 조사 및 연구 2. 보안공학에 대한 응용기술 연구 및 발표 3. 보안공학에 관한 각종 학술 발표회 및 전시회 개최 4. 보안공학 기술의 상호 협조 및 정보교환 5. 보안공학에 관한 표준화 사업 및 규격의 제정 6. 보안공학에 관한 산학연 협동의 증진 7. 국제적 학술 교류 및 기술 협력 8. 보안공학에 관한 논문지 발간 9. 기타 본 회 목적 달성에 필요한 사업
간행물
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- 간행물명
- International Journal of u- and e- Service, Science and Technology
- 간기
- 격월간
- pISSN
- 2005-4246
- 수록기간
- 2008~2016
- 십진분류
- KDC 505 DDC 605
이 권호 내 다른 논문 / International Journal of u- and e- Service, Science and Technology Vol.8 No.10
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