面向本地和外地用户情感分析推荐模型

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

河北大学学报(自然科学版) ›› 2021, Vol. 41 ›› Issue (4): 419-425.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

面向本地和外地用户情感分析推荐模型

魏宁¹,袁方^1,2,刘宇¹

收稿日期:2020-07-29 发布日期:2021-09-03
通讯作者: 袁方(1965—)
作者简介:魏宁(1994—),男,山东枣庄人,河北大学在读硕士研究生,主要从事数据挖掘方向研究.E-mail:858651011@qq.com
基金资助:
河北省科技计划项目(19K57623D)

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

WEI Ning¹,YUAN Fang^1,2,LIU Yu¹

1. College of Mathematics and Information Science, Hebei University, Baoding 071002, China; 2. Computer Science Teaching Department, Hebei University, Baoding 071002, China

Received:2020-07-29 Published:2021-09-03

摘要/Abstract

摘要： 针对地理标签和评论信息的情感倾向对于推荐系统性能的影响,本文基于地理标签和用户评论情感分析提出有关兴趣点的推荐策略,并建立了一种基于内容的推荐模型.本系统首先对用户兴趣点信息进行有效的补充,并实现了用户兴趣点相似度度量.对无标签评论数据进行情感分析及挖掘,获取其情感倾向度.同时本系统结合了时间滑动窗口,更准确地把握用户评论和兴趣点的结合度.最终得到用户个性化推荐排名.本文方法涵盖了本地用户和外地用户的个性化推荐策略.通过实验数据表明,本文模型有效提高了推荐的准确度.

关键词: 地理位置, 基于内容的推荐, 情感分析, 循环神经网络

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

中图分类号:

TP391

魏宁,袁方,刘宇. 面向本地和外地用户情感分析推荐模型[J]. 河北大学学报(自然科学版), 2021, 41(4): 419-425.

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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面向本地和外地用户情感分析推荐模型

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

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