document: Latent aspect rating analysis without aspect keyword supervision 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 .
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