TY - GEN
T1 - Latent aspect rating analysis without aspect keyword supervision
AU - Wang, Hongning
AU - Lu, Yue
AU - Zhai, Cheng Xiang
PY - 2011
Y1 - 2011
N2 - Mining detailed opinions buried in the vast amount of review text data is an important, yet quite challenging task with widespread applications in multiple domains. Latent Aspect Rating Analysis (LARA) refers to the task of inferring both opinion ratings on topical aspects (e.g., location, service of a hotel) and the relative weights reviewers have placed on each aspect based on review content and the associated overall ratings. A major limitation of previous work on LARA is the assumption of pre-specified aspects by keywords. However, the aspect information is not always available, and it may be difficult to pre-define appropriate aspects without a good knowledge about what aspects are actually commented on in the reviews. In this paper, we propose a unified generative model for LARA, which does not need pre-specified aspect keywords and simultaneously mines 1) latent topical aspects, 2) ratings on each identified aspect, and 3) weights placed on different aspects by a reviewer. Experiment results on two different review data sets demonstrate that the proposed model can effectively perform the Latent Aspect Rating Analysis task without the supervision of aspect keywords. Because of its generality, the proposed model can be applied to explore all kinds of opinionated text data containing overall sentiment judgments and support a wide range of interesting application tasks, such as aspect-based opinion summarization, personalized entity ranking and recommendation, and reviewer behavior analysis.
AB - Mining detailed opinions buried in the vast amount of review text data is an important, yet quite challenging task with widespread applications in multiple domains. Latent Aspect Rating Analysis (LARA) refers to the task of inferring both opinion ratings on topical aspects (e.g., location, service of a hotel) and the relative weights reviewers have placed on each aspect based on review content and the associated overall ratings. A major limitation of previous work on LARA is the assumption of pre-specified aspects by keywords. However, the aspect information is not always available, and it may be difficult to pre-define appropriate aspects without a good knowledge about what aspects are actually commented on in the reviews. In this paper, we propose a unified generative model for LARA, which does not need pre-specified aspect keywords and simultaneously mines 1) latent topical aspects, 2) ratings on each identified aspect, and 3) weights placed on different aspects by a reviewer. Experiment results on two different review data sets demonstrate that the proposed model can effectively perform the Latent Aspect Rating Analysis task without the supervision of aspect keywords. Because of its generality, the proposed model can be applied to explore all kinds of opinionated text data containing overall sentiment judgments and support a wide range of interesting application tasks, such as aspect-based opinion summarization, personalized entity ranking and recommendation, and reviewer behavior analysis.
KW - Aspect identification
KW - Latent rating analysis
KW - Review mining
UR - http://www.scopus.com/inward/record.url?scp=80052655130&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=80052655130&partnerID=8YFLogxK
U2 - 10.1145/2020408.2020505
DO - 10.1145/2020408.2020505
M3 - Conference contribution
AN - SCOPUS:80052655130
SN - 9781450308137
T3 - Proceedings of the ACM SIGKDD International Conference on Knowledge Discovery and Data Mining
SP - 618
EP - 626
BT - Proceedings of the 17th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, KDD'11
PB - Association for Computing Machinery
T2 - 17th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, KDD 2011
Y2 - 21 August 2011 through 24 August 2011
ER -