摘要
随着线上美食交易逐渐普及化,用户评价数据急增,充分利用评价大数据信息,得到其中潜在的价值越来越有必要。传统的情感分析只能整体识别好评或差评,无法从评价中了解用户的深层需求以便精准推荐。针对此问题,本文提出了一种基于多重属性聚类加权输出的循环神经网络模型,该模型根据评价中的属性词挖掘用户的兴趣点与商铺的特点进行情感分析,以此构建推荐算法。测试结果表明,本文提出的模型对于情感分类有较高的准确率与召回率,能较精准地捕捉用户兴趣点,提升了个性化推荐的效果。
With the popularity of online food trading, rapid increase in user evaluation data, it is more and more necessary to make full use of evaluation information and analysis the potential value in a large number of comments. Traditional sentiment analysis can only identify the overall praise or poor evaluation, can not understand the deep demand of users to make accurate recommendations.In order to solve this problem, a recursive neural network for multiple attribute clustering weighting output model is proposed in this paper. According to attribute words,the model excavates characteristics of the user＇ s interest points.A recommendation algorithm based on user interest points and shop＇ s characteristics.The test results show that the proposed model has a high precision and recall for emotional classification, can accurately capture interest point, can improve the effect of personalized recommendation.
作者
郭慧
柳林
刘晓
程鹏
GUO Hui;LIU Lin;LIU Xiao;CHENG Peng(College of Geomatics and Engineering,Shandong University of Science and Technology,Qingdao 266590,China)
出处
《测绘通报》
CSCD
北大核心
2018年第9期55-58,共4页
Bulletin of Surveying and Mapping
基金
山东省自然科学基金(ZR2012FM015)
海岛(礁)测绘技术国家测绘地理信息局重点实验室资助项目(2014B08)
卫星测绘技术与应用国家测绘地理信息局重点实验室经费资助项目(KLAMTA-201407)
关键词
情感分析
循环神经网络
属性聚类
深度学习
个性推荐算法
sentiment analysis
recursive neural network
attribute clustering
deep learning
personality recommendation algorithm
