A simple approach for Aspectbased Recommendation using reviews written in Spanish This paper presents an approach to take advantage of reviews written in Spanish to generate Aspectbased Recommendations. Although diverse approaches have been proposed and evaluated for reviews written in English, there is a lack of proposals to accomplish this task using reviews in Spanish. The proposed approach uses Text Mining techniques and tools to extract item aspects and estimate user preferences for each of them. The estimated preferences then feed a MultiCriteria Recommender System, considering each extracted aspect as a criterion regarding user preference. Preliminary results show that, using aspects extracted from reviews written in Spanish, it is possible to improve the quality of recommendations generated with a traditional algorithm. Due to the simplicity of the proposed approach, it can be easily used by local companies to incorporate Aspectbased Recommendations.

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.