This paper proposes a topic model TMPP (Topic Model based on Phrase Parameter)

which can extract the aspects and associated with their ratings for the evaluated entities in online reviews.TMPP has three characterisitcs:(1)It assumes the review is represented as a bag-of-phrase.(2)It extends the document-topic parameter from the standard LDA as a set of (aspect

rating).(3)It incorporates the prior knowledge.We introduce the physical meaning of each parameter for the TMPP

the generative process for the TMPP and the representation of the prior knowledge.Furthermore

the reason and advantage of incorporating the aspect cluster into the TMPP are presented; the mechanism of obtaining the (aspect

rating) is also given by extracting the aspects and associated with their ratings from the online product reviews.We conduct extensive experiments on a very large real life dataset from taobao.com and find that TMPP can produce high quality (aspect

rating) summarization if each review has an overall rating by comparing the performance between existing baseline models and TMPP.