Framework for Analysis and Feedback of Evaluation Expressions in Restaurant Reviews This paper proposes a framework of the cycle for writing and analyzing restaurant reviews. Our method is designed to extract evaluation expressions specific to dishes from the descriptions around the names of dishes in restaurant reviews. It is common to use restaurant reviews for choosing restaurants and dishes. The amount of reputation for foods on the Web has been growing. However, it is difficult for a user to read a large amount of information and select a dish that meets the userxe2x80x99s taste. Based on the above background, we believe that some specific expressions evaluating dishes should be easily obtained and given feedback to reviewers; those expressions are reasonable to support the writing and understanding the reviews. We focus on two types of expressions frequently occurred with the name of the dish: common expressions for flavours such as sizzle words, and words related to particular dishes such as cooking methods. Also, the expressions evaluating a dish are acquired by using the dependency analysis and the word importance. In our consideration, the evaluation expression specific to the dish seemed to capture the characteristics of the dish better than just using sizzle words.

Personalized Recommendation Model: An Online Comment Sentiment Based Analysis Traditional recommendation algorithms measure usersxe2x80x99 online ratings of goods and services but ignore the information contained in written reviews, resulting in lowered personalized recommendation accuracy. Usersxe2x80x99 reviews express opinions and reflect implicit preferences and emotions towards the features of products or services. This paper proposes a model for the finegrained analysis of emotions expressed in usersxe2x80x99 online written reviews, using film reviews on the Chinese social networking site Douban.com as an example. The model extracts featuresentiment word pairs in user reviews according to four syntactic dependencies, examines film features, and scores the sentiment values of film features according to user preferences. User group personalized recommendations are realized through user clustering and user similarity calculation. Experiments show that the extraction of user featuresentiment word pairs based on four syntactic dependencies can better identify the implicit preferences of users, apply them to recommendations and thereby increase recommendation accuracy.