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Data Analysis and Knowledge Discovery  2019, Vol. 3 Issue (8): 10-20    DOI: 10.11925/infotech.2096-3467.2018.1030
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Sentiment Analysis for Online User Reviews Based on Tripartite Network
Weicong Lu,Jian Xu()
School of Information Management, Sun Yat-Sen University, Guangzhou 510006, China
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Abstract  

[Objective] The paper proposes a tripartite network sentiment analysis method, aiming to reflect the indirect connections between nodes. [Methods] We constructed a “user-product-sentiment tag” tripartite network, which were split into three bipartite networks for network structure analysis. Then, we used the proposed tripartite network projection method to obtain the “two-sentiment one-mode” network of users and products. [Results] We obtained the association of high-weighted related nodes from NetEase Cloud music dataset, and information such as genre classifications, hot-rated songs, and fan groups. [Limitations] The large number of user nodes need to be visualized in the future. [Conclusions] Based on the formation, splitting and projection of the sentiment tripartite network, we present the indirect connection between nodes, and provide new perspectives for network sentiment analysis.

Key wordsTripartite Network      Sentiment Analysis      Network Users’ Comment     
Received: 17 September 2018      Published: 29 September 2019
ZTFLH:  TP393 G35  
Corresponding Authors: Jian Xu     E-mail: issxj@mail.sysu.edu.cn

Cite this article:

Weicong Lu,Jian Xu. Sentiment Analysis for Online User Reviews Based on Tripartite Network. Data Analysis and Knowledge Discovery, 2019, 3(8): 10-20.

URL:

https://manu44.magtech.com.cn/Jwk_infotech_wk3/EN/10.11925/infotech.2096-3467.2018.1030     OR     https://manu44.magtech.com.cn/Jwk_infotech_wk3/EN/Y2019/V3/I8/10

[1] 徐健 . 基于网络用户情感分析的预测方法研究[J]. 中国图书馆学报, 2013,39(3):96-107.
[1] ( Xu Jian . Research on Predicting Methods Based on Network User Sentiment Analysis[J]. Journal of Library Science in China, 2013,39(3):96-107.)
[2] 张红丽, 刘济郢, 杨斯楠 , 等. 基于网络用户评论的评分预测模型研究[J]. 数据分析与知识发现, 2017,1(8):48-58.
[2] ( Zhang Hongli, Liu Jiying, Yang Sinan , et al. Predicting Online Users’ Ratings with Comments[J]. Data Analysis and Knowledge Discovery, 2017,1(8):48-58.)
[3] 梁晓敏, 徐健 . 舆情事件中评论对象的情感分析及其关系网络研究[J]. 情报科学, 2018,36(2):37-42.
[3] ( Liang Xiaomin, Xu Jian . Sentiment Analysis of Objects in Public Opinion Events and Their Relation Network Research[J]. Information Science, 2018,36(2):37-42.)
[4] 李旭, 于卫红 . 基于情感分析和关系网络的影视产品评论数据文本挖掘研究[J]. 情报探索, 2018(4):1-5.
[4] ( Li Xu, Yu Weihong . Text Mining of Comment Dada on Video Electronic Product Based on Sentiment Analysis and Relational Network[J]. Information Research, 2018(4):1-5.)
[5] 王伟, 王洪伟 . 面向竞争力的特征比较网络:情感分析方法[J]. 管理科学学报, 2016,19(9):109-126.
[5] ( Wang Wei, Wang Hongwei . Comparative Network for Product Competition in Feature-Levels Through Sentiment Analysis[J]. Journal of Management Sciences in China, 2016,19(9):109-126.)
[6] 杨洁, 李继云, 姜霖霖 . 基于情感和网络分析的社交网络用户人格预测[J]. 智能计算机与应用, 2016,6(1):50-54.
[6] ( Yang Jie, Li Jiyun, Jiang Linlin . Predicting Personality of Social Network Users Based on Sentiment Analysis and Network Analysis[J]. Intelligent Computer and Applications, 2016,6(1):50-54.)
[7] Thirumalai D . Sentimental Bi-Partite Graph of Political Blogs[D]. Phoenix: Arizona State University, 2012.
[8] 王志昊, 王中卿, 李寿山 , 等. 面向半监督情感分类的特征选择方法研究[J]. 中文信息学报, 2013,27(6):96-102.
[8] ( Wang Zhihao, Wang Zhongqing, Li Shoushan , et al. Feature Selection Method for Semi-Supervised Sentiment Classification[J]. Journal of Chinese Information Processing, 2013,27(6):96-102.)
[9] 杨源, 马云龙, 林鸿飞 . 评论挖掘中产品属性归类问题研究[J]. 中文信息学报, 2012,26(3):104-108.
[9] ( Yang Yuan, Ma Yunlong, Lin Hongfei . Clustering Product Features in Opinion Mining[J]. Journal of Chinese Information Processing, 2012,26(3):104-108.)
[10] 卢伟聪, 徐健 . 基于二分网络的网络用户评论情感分析[J]. 情报理论与实践, 2018,41(2):121-126.
[10] ( Lu Weicong, Xu Jian . Sentiment Analysis of Network Users’ Reviews Based on Bipartite Network[J]. Information Studies:Theory & Application, 2018,41(2):121-126.)
[11] Fararo T J, Doreian P . Tripartite Structural Analysis: Generalizing the Breiger-Wilson Formalism[J]. Social Networks, 1984,6(2):141-175.
[12] 许明, 吴建平, 杜怡曼 , 等. 基于三部图的路网节点关键度排序方法[J]. 北京邮电大学学报, 2014,37(S1):51-54.
[12] ( Xu Ming, Wu Jianping, Du Yiman , et al. A Method of Key Node Ranking for Road Network Based on Tripartite Graph[J]. Journal of Beijing University of Posts and Telecommunications, 2014,37(S1):51-54.)
[13] 王道平, 周丹云, 李秀雅 . 基于三部图的随机游走知识推送方法研究[J]. 情报杂志, 2013,32(9):185-189.
[13] ( Wang Daoping, Zhou Danyun, Li Xiuya . Study on Knowledge Push Method Based on Tripartite Graphs Random Walk with Restart[J]. Journal of Intelligence, 2013,32(9):185-189.)
[14] 肖扬, 王道平, 杨岑 . 基于三部图网络结构的知识推荐算法[J]. 计算机应用研究, 2015,32(2):386-390.
[14] ( Xiao Yang, Wang Daoping, Yang Cen . Study on Knowledge Recommendation Algorithm Based on Tripartite Graphs Network Structure[J]. Application Research of Computers, 2015,32(2):386-390.)
[15] 李贵, 王爽, 李征宇 , 等. 基于时间加权三部图的分众分类标签推荐算法[J]. 小型微型计算机系统, 2016,37(2):269-274.
[15] ( Li Gui, Wang Shuang, Li Zhengyu , et al. Folksonomy Tag Recommendation Algorithm Based on a Time-weighted Tripartite Graph[J]. Journal of Chinese Computer Systems, 2016,37(2):269-274.)
[16] 胡吉明, 林鑫 . 基于用户-资源-词汇三部图的社会化推荐算法设计与实现[J]. 情报理论与实践, 2016,39(3):130-134.
[16] ( Hu Jiming, Lin Xin . Design and Implementation of Social Recommendation Algorithm Based on User-Object-Topic Tripartite Graph[J]. Information Studies: Theory & Application, 2016,39(3):130-134.)
[17] 廖志芳, 李玲, 刘丽敏 , 等. 三部图张量分解标签推荐算法[J]. 计算机学报, 2012,35(12):2625-2632.
[17] ( Liao Zhifang, Li Ling, Liu Limin , et al. A Tripartite Decomposition of Tensor for Social Tagging[J]. Chinese Journal of Computers, 2012,35(12):2625-2632.)
[18] 陈超, 张颖超, 缪进 . 一种基于三部图网络的协同过滤算法[J]. 南京信息工程大学学报: 自然科学版, 2010,2(4):337-339.
[18] ( Chen Chao, Zhang Yingchao, Miao Jin . A Collaborative Filtering Recommender Algorithm Based on Tripartite Network[J]. Journal of Nanjing University of Information Science & Technology: Natural Science Edition, 2010,2(4):337-339.)
[19] Nazir F, Takeda H . Extraction and Analysis of Tripartite Relationships from Wikipedia [C]// Proceedings of the 2008 IEEE International Symposium on Technology and Society. IEEE, 2008: 1-13.
[20] Murata T . Detecting Communities from Tripartite Networks [C]// Proceedings of the 19th International Conference on World Wide Web. ACM, 2010: 1159-1160.
[21] Lu C, Chen X, Park E K . Exploit the Tripartite Network of Social Tagging for Web Clustering [C]// Proceedings of the 18th ACM Conference on Information and Knowledge Management. ACM, 2009: 1545-1548.
[22] Lambiotte R, Ausloos M . Collaborative Tagging as a Tripartite Network [C]// Proceedings of the 6th International Conference on Computational Science. Springer, 2006: 1114-1117.
[23] 孟佳娜, 于玉海, 赵丹丹 , 等. 特征和实例迁移相融合的跨领域倾向性分析[J]. 中文信息学报, 2015,29(4):74-79, 143.
[23] ( Meng Jiana, Yu Yuhai, Zhao Dandan , et al. Cross-domain Sentiment Analysis Based on Combination of Feature and Instance-transfer[J]. Journal of Chinese Information Processing, 2015,29(4):74-79, 143.)
[24] Wu Q, Tan S, Zhai H , et al. SentiRank: Cross-Domain Graph Ranking for Sentiment Classification [C]// Proceedings of the 2009 IEEE/WIC/ACM International Joint Conference on Web Intelligence and Intelligent Agent Technology. IEEE, 2009,1:309-314.
[25] Zhu L, Galstyan A, Cheng J , et al. Tripartite Graph Clustering for Dynamic Sentiment Analysis on Social Media [C]// Proceedings of the 2014 ACM SIGMOD International Conference on Management of Data. ACM, 2014: 1531-1542.
[26] 欧瑞秋, 杨建梅, 常静 . 企业-产品二分网络的社团结构分析——以中国汽车产业为例[J]. 管理学报, 2010,7(9):1403-1409.
[26] ( Ou Ruiqiu, Yang Jianmei, Chang Jing . Community Structure Analysis of Firm-product Bipartite Network: A Case Study on China’s Automobile Industry[J]. Chinese Journal of Management, 2010,7(9):1403-1409.)
[27] 杨亮, 林原, 林鸿飞 . 基于情感分布的微博热点事件发现[J]. 中文信息学报, 2012,26(1):84-91.
[27] ( Yang Liang, Lin Yuan, Lin Hongfei . Micro-Blog Hot Events Detection Based on Emotion Distribution[J]. Journal of Chinese Information Processing, 2012,26(1):84-91.)
[28] 史伟, 王洪伟, 何绍义 . 基于语义的中文在线评论情感分析[J]. 情报学报, 2013,32(8):860-867.
[28] ( Shi Wei, Wang Hongwei, He Shaoyi . Sentiment Analysis of Chinese Online Reviews Based on Semantics[J]. Journal of the China Society for Scientific and Technical Information, 2013,32(8):860-867.)
[29] 网易云音乐[EB/OL]. [2018-01-18].
[29] ( NetEase Cloud Music[EB/OL]. [2018-01-18]. )
[30] 徐琳宏, 林鸿飞, 潘宇 , 等. 情感词汇本体的构造[J]. 情报学报, 2008,27(2):180-185.
doi: 10.3969/j.issn.1000-0135.2008.02.004
[30] ( Xu Linhong, Lin Hongfei, Pan Yu , et al. Constructing the Affective Lexicon Ontology[J]. Journal of the China Society for Scientific and Technical Information, 2008,27(2):180-185.)
doi: 10.3969/j.issn.1000-0135.2008.02.004
[31] Blondel V D, Guillaume J L, Lambiotte R , et al. Fast Unfolding of Communities in Large Networks[J]. Journal of Statistical Mechanics: Theory and Experiment, 2008(10):155-168.
[32] Mukherjee A, Choudhury M, Peruani F , et al. Dynamics on and of Complex Networks, Volume 2[M]. Springer New York, 2013.
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