With the prevalence of Web 2.0, people increasingly prefer to express opinions and exchange information through CGM (consumer-generated media), such as blog, Internet forum and etc. Many studies pay attention to extract and analysis user opinions in consumer reviews. This paper studies how to automatically extract Chinese comparative sentences from consumer reviews. At first, the paper describes a method for solving the class imbalance problem of comparatives and non-comparatives in review data. Then we built a support vector machine learning model to classify comparatives and non-comparatives into different group on a balanced dataset. Experiments were conducted on consumer-generated product reviews, including 9600 sentences, of which 1,624 (16.92% of the total) were comparisons. Experiments show an overall F-score of 87.26%, which presents the effectiveness of the proposed approach.
목차
Abstract 1. Introduction 2. Related Work 3. Feature Representations 3.1. Feature Sets 1: Term Features 3.2. Feature Sets 2: Comparative Keywords 3.3. Feature Sets 3: Frequent Sequences 3.4. Feature Sets 4: Infrequent Sequences 4. Classification Learning 5. Experimental Evaluation 5.1. Data Sets 6. Conclusion and Future Work Acknowledgement References
보안공학연구지원센터(IJGDC) [Science & Engineering Research Support Center, Republic of Korea(IJGDC)]
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
간행물명
International Journal of Grid and Distributed Computing
간기
격월간
pISSN
2005-4262
수록기간
2008~2016
십진분류
KDC 505DDC 605
이 권호 내 다른 논문 / International Journal of Grid and Distributed Computing Vol.9 No.3