Please wait a minute...
New Technology of Library and Information Service  2015, Vol. 31 Issue (11): 33-40    DOI: 10.11925/infotech.1003-3513.2015.11.06
Current Issue | Archive | Adv Search |
Hot Topic Extraction from E-commerce Microblog Based on EM-LDA Integrated Model
Wu Wankun1, Wu Qinglie1, Gu Jinjiang1,2
1 School of Economics and Management, Southeast University, Nanjing 211189, China
2 Department of Information Technology, Jiangsu Institute of Commerce, Nanjing 211168, China
Download: PDF(620 KB)   HTML  
Export: BibTeX | EndNote (RIS)      

[Objective] Extract hot topics from e-commerce microblog in social marketing.[Methods] This paper proposes an integrated model, EM-LDA (E-commerce Microblog-LDA) to extract hot topics from e-commerce microblog. The integrated model contains two submodels, that is, ET-LDA model and IT-LDA model. The former is to extract hot topics from those e-commerce microblog with Hashtag, and the latter is to extract hot topics from those e-commerce microblog without Hashtag.[Results] The standard LDA model and EM-LDA integrated model are both used to extract hot topics from e-commerce microblog text after the number of topics is determined. Compared with the standard LDA model, EM-LDA model extract hot topics more accurately and effectively, also can improve interpretability.[Limitations] ET-LDA model is not considered about the relationship between microblog contacts, that is, user feature is neglected. IT-LDA model does not concern how to deal with those e-commerce microblog both belong to conversation and retweet.[Conclusions] According to the special features of e-commerce microblog text, EM-LDA integrated model ameliorates the standard LDA model to improve the accuracy of hot topic extraction from e-commerce microblog.

Received: 27 May 2015      Published: 06 April 2016
:  TP393  

Cite this article:

Wu Wankun, Wu Qinglie, Gu Jinjiang. Hot Topic Extraction from E-commerce Microblog Based on EM-LDA Integrated Model. New Technology of Library and Information Service, 2015, 31(11): 33-40.

URL:     OR

[1] Wei X, Croft W B. LDA-based Document Models for Ad-hoc Retrieval [C]. In: Proceedings of the 29th Annual International ACM SIGIR Conference on Research and Development in Information Retrieval. ACM, 2006: 178-185.
[2] 张晨逸, 孙建伶, 丁轶群. 基于MB-LDA模型的微博主题挖掘[J]. 计算机研究与发展, 2011, 48(10): 1795-1802. (Zhang Chenyi, Sun Jianling, Ding Yiqun. Topic Mining for Microblog Based on MB-LDA Model [J]. Journal of Computer Research and Development, 2011, 48(10): 1795-1802.)
[3] 张晓艳, 王挺, 梁晓波. LDA模型在话题追踪中的应用[J]. 计算机科学, 2011, 38(10A): 136-139, 152. (Zhang Xiaoyan, Wang Ting, Liang Xiaobo. Use of LDA Model in Topic Tracking [J]. Computer Science, 2011, 38(10A): 136-139, 152.)
[4] 张培晶, 宋蕾. 基于LDA的微博文本主题建模方法研究述评[J]. 图书情报工作, 2012, 56(24): 120-126. (Zhang Peijing, Song Lei. Overview on Topic Modeling of Microblogs Text Based on LDA [J]. Library and Information Service, 2012, 56(24): 120-126.)
[5] Weng J, Lim E P, Jiang J, et al. TwitterRank: Finding Topic-sensitive Influential Twitterers [C]. In: Proceedings of the 3rd ACM International Conference on Web Search and Data Mining. ACM, 2010: 261-270.
[6] Hong L, Davison B D. Empirical Study of Topic Modeling in Twitter [C]. In: Proceedings of the 1st Workshop on Social Media Analytics. ACM, 2010: 80-88.
[7] Rosen-Zvi M, Griffiths T, Steyvers M, et al. The Author-topic Model for Authors and Documents [C]. In: Proceedings of the 20th Conference on Uncertainty in Artificial Intelligence. AUAI Press, 2004: 487-494.
[8] Zhao W X, Jiang J, Weng J, et al. Comparing Twitter and Traditional Media Using Topic Models [C]. In: Proceedings of the 33rd European Conference on Informatin Retrieval. Springer Berlin Heidelberg, 2011: 338-349.
[9] Ramage D, Hall D, Nallapati R, et al. Labeled LDA: A Supervised Topic Model for Credit Attribution in Multi-labeled Corpora [C]. In: Proceedings of the 2009 Conference on Empirical Methods in Natural Language Processing. 2009: 248-256.
[10] 唐晓波, 向坤. 基于LDA模型和微博热度的热点挖掘[J]. 图书情报工作, 2014, 58(5): 58-63. (Tang Xiaobo, Xiang Kun. Hotspot Mining Based on LDA Model and Microblog Heat [J]. Library and Information Service, 2014, 58(5): 58-63.)
[11] 朱颖. 基于微博的热点话题发现[D]. 重庆: 西南大学, 2014. (Zhu Ying. Hot Topic Extraction from Microblogs [D]. Chongqing: Southwest University, 2014.)
[12] Blei D M, Ng A Y, Jordan M I. Latent Dirichlet Allocation [J]. Journal of Machine Learning Research, 2003, 3: 993-1022.
[13] Rosen-Zvi M, Chemudugunta C, Griffiths T, et al. Learning Author-topic Models from Text Corpora [J]. ACM Transactions on Information Systems, 2010, 28(1): Article No.4.
[14] Zhao W X, Jiang J, He J, et al. Topical Keyphrase Extraction from Twitter [C]. In: Proceedings of the 49th Annual Meeting of the Association for Computational Linguistics. 2011: 379-388.
[15] Ramage D, Dumais S T, Liebling D J. Characterizing Microblogs with Topic Models [C]. In: Proceedings of the 4th International Conference on Weblogs and Social Media. 2010.
[16] 王星. 大数据分析: 方法与应用[M]. 北京: 清华大学出版社, 2013: 287-289. (Wang Xing. Big Data Analysis: Methods and Applications [M]. Beijing: Tsinghua University Press, 2013: 287-289.)
[17] 数据堂. 50条热门微博的所有转发和评论[EB/OL]. [2015-03-29]. (Datatang. All Retweets and Comments of 50 Hot Microblogs [EB/OL]. [2015-03-29].
[18] 数据堂. 63641个用户的新浪微博数据集[EB/OL]. [2015-03-30]. (Datatang. Sina Microblog Datasets of 63641 Users [EB/OL]. [2015-03-30].
[19] Toyabe T, Asai S. Analytical Models of Threshold Voltage and Breakdown Voltage of Short-channel MOSFET's Derived from Two-dimensional Analysis [J]. IEEE Transactions on Electron Devices, 1979, 26(4): 453-461.
[20] Cao J, Xia T, Li J, et al. A Density-based Method for Adaptive LDA Model Selection [J]. Neurocomputing, 2009, 72(7-9): 1775-1781.

[1] Qingtian Zeng,Xiaohui Hu,Chao Li. Extracting Keywords with Topic Embedding and Network Structure Analysis[J]. 数据分析与知识发现, 2019, 3(7): 52-60.
[2] Lixin Xia,Jieyan Zeng,Chongwu Bi,Guanghui Ye. Identifying Hierarchy Evolution of User Interests with LDA Topic Model[J]. 数据分析与知识发现, 2019, 3(7): 1-13.
[3] Yang Ning, Huang Feihu, Wen Yi, Chen Yunwei. An Opinion Evolution Model Based on the Behavior of Micro-blog Users[J]. 现代图书情报技术, 2015, 31(12): 34-41.
[4] Yu Xincong, Li Honglian, Lv Xueqiang. Research on the Application of Hyponymy in the Enrollment Robot[J]. 现代图书情报技术, 2015, 31(12): 65-71.
[5] Wang Zhengjun, Yu Xiaoyi, Jin Yuling. Using Sniffer Technology to Constraint Electronic Resource Excessive Downloading[J]. 现代图书情报技术, 2015, 31(12): 95-100.
[6] Liu Zhanbing, Xiao Shibin. Collaborative Filtering Recommended Algorithm Based on User's Interest Fuzzy Clustering[J]. 现代图书情报技术, 2015, 31(11): 12-17.
[7] Qiang Shaohua, Wu Peng. The Research of Spatial Measure of Users' Mental Model of Website Category from the View of Regional Differences[J]. 现代图书情报技术, 2015, 31(11): 68-74.
[8] Qin Xuedong. Solution for KVM Private Cloud Management System Based on Drupal[J]. 现代图书情报技术, 2015, 31(11): 91-95.
[9] Wu Jiang, Zhang Jinfan. Research on Follow Influence of Triadic Structure in Social Network——Take Student Relation Network as an Example[J]. 现代图书情报技术, 2015, 31(10): 72-80.
[10] Jiang Chuntao. Automatic Annotation of Bibliographical References in Chinese Patent Documents[J]. 现代图书情报技术, 2015, 31(10): 81-87.
[11] Wang Ying, Zhang Zhixiong, Li Chuanxi, Liu Yi, Tang Yijie, Zhou Zijian, Qian Li, Fu Honghu. The Design and Implementation of Open Engine System for Scientific & Technological Knowledge Organization Systems[J]. 现代图书情报技术, 2015, 31(10): 95-101.
[12] Gui Sisi, Lu Wei, Huang Shihao, Zhou Pengcheng. User Interest Prediction Combing Topic Model and Multi-time Function[J]. 现代图书情报技术, 2015, 31(9): 9-16.
[13] Qin Xiaohui, Le Xiaoqiu. Topic Sources and Trends Tracking Towards Citation Network of Single Paper[J]. 现代图书情报技术, 2015, 31(9): 52-59.
[14] Deng Qiping, Wang Xiaomei. Identifying Influential Authors Based on LeaderRank[J]. 现代图书情报技术, 2015, 31(9): 60-67.
[15] Zheng Haishan. The Automatic System for Infrastructure Deployment in the Data Center of Library[J]. 现代图书情报技术, 2015, 31(9): 97-101.
  Copyright © 2016 Data Analysis and Knowledge Discovery   Tel/Fax:(010)82626611-6626,82624938