融合用户评论和异构图神经网络的景点分类  

Attraction Classification Based on User Reviews and Heterogeneous Graph Neural Network

作　　者：李山山 郭景峰[1,2] 郑超 魏宁[1,2] 张丽艳 LI Shan-shan;GUO Jing-feng;ZHENG Chao;WEI Ning;ZHANG Li-yan(College of Information Science and Engineering,Yanshan University,Qinhuangdao 066004,China;The Key Laboratory for Computer Virtual Technology and System Integration of Hebei Province,Qinhuangdao 066004,China)

机构地区：[1]燕山大学信息科学与工程学院,河北秦皇岛066004 [2]河北省计算机虚拟现实技术与系统集成重点实验室,河北秦皇岛066004

出　　处：《小型微型计算机系统》2023年第9期1884-1891,共8页Journal of Chinese Computer Systems

基　　金：国家自然科学基金项目(62172352,61871465)资助;河北省自然科学基金项目(F2019203157)资助;河北省高等学校科学技术研究重点项目(ZD2019004)资助;河北省重点研发计划项目(20310301D)资助.

摘　　要：科技的发展使得数据量呈爆炸式增长,从数据中挖掘有价值的信息成为各行业研究的热点.旅游景点类型的准确划分,对于推动文化旅游产业发展具有重要意义.对此,论文融合景点评论构建景点异质信息网络,提出了SGAE(Scenic Spot Heterogeneous Graph Attention Embedding)模型.首先,从旅游网站和百科网站爬取国内部分5A和部分4A景点的描述以及景点的评论数据,通过对数据的处理和分析,从评论中挖掘出10个相关主题,构建由景点名称、景点评论和评论主题组成的异质信息网络;其次,将不同类型节点信息映射到同一空间,构造异质图卷积的逐层传播规则;然后,根据邻居节点的类型和节点的不同对某一具体节点的影响不同,将双层注意力引入异质图卷积网络中,提出SGAE模型,学习景点名称的低维特征表示,通过Softmax函数进行归一化,确定景点类型;最后,在景点数据集上与经典分类算法对比,所提出的SGAE模型在准确率和F1值较当前最优方法分别提高5%和4%,在公共数据集AGNews和MR上SGAE模型性能优于所有对比模型,且与分类效果最好的HGCN-RN模型相比,SGAE在AGNews上准确率和F1值分别提升了1.95%和1.98%,在MR上准确率和F1值分别提升了3.92%和6.96%,充分验证了论文所提算法在分类任务上的有效性.总之,针对景点分类问题,论文所提出的SGAE模型有效的提高了旅游景点分类问题的效果,具有较好的应用前景.With the development of science and technology,the amount of data increases explosively.Mining valuable information from data become a research hotspot in various industries.The accurate classification of the types of tourist attractions is of great significance to promote the development of cultural tourism industry.In this paper,the heterogeneous information network of scenic spots is constructed by integrating scenic spot reviews,and the SGAE model is proposed.Firstly,the description of some domestic 5A and 4A scenic spots and the review data of scenic spots are crawled from tourism websites and encyclopedia websites.Through the processing and analysis of the data,10 related topics are mined from the reviews,and a heterogeneous information network which composed of scenic spot names,scenic spot reviews and review topics is constructed;Secondly,different types of node information are mapped to the same space,and the layer-wise propagation rules of heterogeneous graph convolution are constructed.Then,depending on the impact of different types of neighbor nodes and different nodes on a special node is different,the double-layer attention is introduced into the heterogeneous graph convolution network,and the SGAE model is proposed to learn the low-dimensional feature representation of scenic spot names,and the scenic spot types are determined by normalization through Softmax function.Finally,compared with the classical classification algorithm on the scenic spot data set,the accuracy and F1 value of the proposed SGAE model are improved by 5%and 4%respectively compared with the current optimal method.On the public data set AGNews and MR,the performance of SGAE model is better than all comparison models,and compared with the HGCN-RN model with the best classification effect,the accuracy and F1 value of SGAE on AGNews are improved by 1.9447%and 1.975%respectively,On MR,the accuracy and F1 value are improved by 3.92%and 6.96%respectively,which fully verifies the effectiveness of the proposed algorithm in classificatio

关 键 词：异构图网络 图卷积神经网络 注意力机制 表示学习 评论文本 

分 类 号：TP302[自动化与计算机技术—计算机系统结构]