Earticle

초록

영어

Sentiment analysis based on the aspects of products or services is designed to explore subjective information such as attitudes and opinions in user-generated reviews. Although a great many of approaches have been proposed in detecting aspects and the relevant aspect-specific sentiments, most of them detect the latent aspects with no proper classifying them or classify them employing unsupervised topic modeling without predicting the sentiment towards these aspects. This paper proposes a novel sentiment-aspect analysis probabilistic modeling framework consisting of Seeding words extraction and semi-supervised topic (SST) model based on Sentence-LDA. More specifically, the proposed methodology starts by capturing seeding words from the websites inherent semi-structured information about products or services description. Then, it employs the captured seeding words to instruct the discovery of aspects and relevant sentiment of products or services simultaneously. Experimental results show that significant improvements have been achieved by the proposed method with respect to other state-of-the-art methods.

목차

Abstract
 1 Introduction
 2. Proposed Approach
  2.1. Seeding Words and Regrouping
  2.2. SST Model
 3. Experiences
  3.1. Dataset and Settings
  3.2. Sentiment-Aspect Extraction
  3.3. Predictive Power
  3.4. Sentiment Classification
 4. Conclusions and Future Work
 References

키워드

저자

• Yong Heng Chen [ College of Computer Science, Minnan Normal University, Zhang zhou 363000, China ]
• Wanli Zuo [ College of Computer Science and Technology, Jilin University, Changchun, China ]
• Hao Yue [ College of Computer Science, Minnan Normal University, Zhang zhou 363000, China ]
• Yaojin Lin [ College of Computer Science, Minnan Normal University, Zhang zhou 363000, China ]

참고문헌

발행기관

간행물

표지보기 관심저널 등록

• 간행물명

  International Journal of Database Theory and Application

• 간기

  격월간

• pISSN

  2005-4270

• 수록기간

  2008~2016

• 십진분류

  KDC 505 DDC 605