摘要
[目的/意义]随着社会化媒体的兴起,信息资源的数量呈现爆炸式增长,如何在海量的信息中帮助用户发现有用的知识成为亟须解决的问题。互联网上已经存在的各类用户评论信息中蕴含着大量的可再开发的知识资源,包括用户的个人信息、选择偏好和消费习惯等,有助于解决"信息过载"问题。[方法/过程]文章通过对豆瓣电影评论信息进行细粒度的情感分析进而有效地获取集体智慧,并且利用评论挖掘技术发掘用户的偏好,为用户选择产品提供更加有效的推荐策略。[结果/结论]实验表明,将大众智慧与个性化服务两者有机地结合起来,能够真实地反映出不同用户对于电影的感受特性,并为用户观影提供更加合理的参考。
[Purpose/significance] With the booming of social media,the number of information resources has been exploding. How to help users find useful information in vast amount of information becomes an urgent problem to be solved. The reviews of internet users contain lots of knowledge resources waited to be developed,including users ＇personal information,preferences,consumptive habits,and so on,which can solve the problem of information overload. [Method/process] The paper makes a finegrained sentimental analysis of film reviews on douban. com to get the collective wisdom effectively. Through mining technology from reviews to explore users＇ preference,the paper can provide a more effective recommendation strategy for users choosing products.[Result/conclusion]Experiment shows that the combination of the wisdom of the public and personalized service can truly reflect the feelings of different users and provide a more reasonable reference for the users.
出处
《情报理论与实践》
CSSCI
北大核心
2017年第8期99-104,共6页
Information Studies:Theory & Application
关键词
评论挖掘
情感分析
用户评论
个性化推荐
信息过载
opinion mining
sentimental analysis
user review
personalized recommendation
information overload
