2010 LatentAspectRatingAnalysisonRev
- (Wang et al., 2010) ⇒ Hongning Wang, Yue Lu, and Chengxiang Zhai. (2010). “Latent Aspect Rating Analysis on Review Text Data: A Rating Regression Approach.” In: Proceedings of the 16th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining (KDD-2010). doi:10.1145/1835804.1835903
Subject Headings:
Notes
- Categories and Subject Descriptors H.3.3 Information Search and Retrieval: Text Mining.
- General Terms: Algorithms, Experimentation
Cited By
- http://scholar.google.com/scholar?q=%22Latent+aspect+rating+analysis+on+review+text+data%3A+a+rating+regression+approach%22+2010
- http://portal.acm.org/citation.cfm?id=1835903&preflayout=flat#citedby
Quotes
Author Keywords
Opinion and sentiment analysis, review mining, latent rating analysis
Abstract
In this paper, we define and study a new opinionated text data analysis problem called Latent Aspect Rating Analysis (LARA), which aims at analyzing opinions expressed about an entity in an online review at the level of topical aspects to discover each individual reviewer's latent opinion on each aspect as well as the relative emphasis on different aspects when forming the overall judgment of the entity. We propose a novel probabilistic rating regression model to solve this new text mining problem in a general way. Empirical experiments on a hotel review data set show that the proposed latent rating regression model can effectively solve the problem of LARA, and that the detailed analysis of opinions at the level of topical aspects enabled by the proposed model can support a wide range of application tasks, such as aspect opinion summarization, entity ranking based on aspect ratings, and analysis of reviewers rating behavior.
References
,
Author | volume | Date Value | title | type | journal | titleUrl | doi | note | year | |
---|---|---|---|---|---|---|---|---|---|---|
2010 LatentAspectRatingAnalysisonRev | ChengXiang Zhai Hongning Wang Yue Lu | Latent Aspect Rating Analysis on Review Text Data: A Rating Regression Approach | KDD-2010 Proceedings | 10.1145/1835804.1835903 | 2010 |