Recommendation model of sentiment analysis for both home-town and out-of-town users

doi:10.3969/j.issn.1000-1565.2021.04.012面向本地和外地用户情感分析推荐模型

Abstract

Abstract: Aiming at the influence of the sentiment tendency of geographical position and comments on the performance of the recommendation system,this paper proposes a recommendation strategy for points-of-interest based on geographical position and sentiment analysis of user reviews, and establishes a content-based recommendation model.First,the system effectively supplements the user’s point-of-interest information, and realizes the similarity measurement of the user’s point-of-interest. Secondly,emotional analysis and mining of unlabeled comment data are carried out to obtain the sentimental tendency. At the same time, the system combines a time sliding window, and more accurately grasp the combination of user comments and points-of-interest. Finally, the personalized recommendation ranking of users is obtained. In this paper the method cover the personalized recommendation strategies of home-town users and out-of-town users. The experimental data shows that the model in this paper effectively improves the accuracy of the recommendation.

Key words: geographical position, content-based recommendations, sentiment analysis, recurrent neural network

CLC Number:

TP391

WEI Ning,YUAN Fang,LIU Yu. Recommendation model of sentiment analysis for both home-town and out-of-town users[J]. Journal of Hebei University(Natural Science Edition), 2021, 41(4): 419-425.

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