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Electronic Commerce Research
Erschienen in: Electronic Commerce Research 1/2017

25.07.2016

Sentiment community detection: exploring sentiments and relationships in social networks

verfasst von: Dong Wang, Jiexun Li, Kaiquan Xu, Yizhen Wu

Erschienen in: Electronic Commerce Research | Ausgabe 1/2017

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Abstract

Social networking sites (SNS), which allow users to express opinions on products/services, have become an important channel and platform for enterprises to acquire and trace users’ sentiments in order to design appropriate business strategies and online marketing campaigns. However, with the large number of users and complex user relationships on SNS, effectively capturing these sentiments for business decision support is still a big challenge. In this study we introduce the concept of “Sentiment Community,” a group of users who are closely connected and highly consistent in their sentiments about one product/service. Discovering such sentiment communities would be very valuable to enterprises for customer segmentation and target marketing. Taking into account both connections and sentiments, we propose two methods to discover sentiment communities by adopting the optimization models of semi-definite programming (SDP). Our experimental evaluations demonstrated great performances for the proposed methods. This study opens the doors to effectively explore users’ sentiments on SNS for business decision making.
Vorheriger Artikel Effects of the aesthetic design of icons on app downloads: evidence from an android market
Nächster Artikel The nature of sellers’ cyber credit in C2C e-commerce: the perspective of social capital

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Literatur
1.
Abbasi, A., Chen, H., & Salem, A. (2008). Sentiment analysis in multiple languages: Feature selection for opinion classification in Web forums. ACM Transactions on Information Systems, 26(3), 1–34.CrossRef
2.
Agarwal, G., & Kempe, D. (2008). Modularity-maximizing graph communities via mathematical programming. The European Physical Journal B, 66(3), 409–418.CrossRef
3.
Akoglu, L., Tong, H., Meeder, B., & Faloutsos, C. (2012). PICS: Parameter-free identification of cohesive subgroups in large attributed graphs. In Proceedings of the 2012 SIAM international conference on data mining, Anaheim, CA, 26–28, April 2012.
4.
Bansal, N., Blum, A., & Chawla, S. (2004). Correlation clustering. Machine Learning, 56(1–3), 86–113.
5.
Cao, T., Wu, X., Wang, S., & Hu, X. (2011). Maximizing influence spread in modular social networks by optimalresource allocation. Expert Systems with Applications, 38, 13128–13135.CrossRef
6.
Chau, M., & Xu, J. (2007). Mining communities and their relationships in blogs: A study of online hate groups. International Journal of Human-Computer Studies, 65(1), 57–70.CrossRef
7.
Chen, H. (2006). Intelligence and security informatics: information systems perspective. Decision Support Systems, 41(3), 555–559.CrossRef
8.
Chen, L., & Wang, F. (2014). Sentiment-enhanced explanation of product recommendations. In Proceedings of the 23rd international conference on World Wide Web, Seoul, Korea, April 7–11, 2014, pp. 239–240.
9.
Chevalier, J., & Mayzlin, D. (2006). The Effect of word of mouth on sales: Online book reviews. Journal of Marketing Research, 63, 345–354.CrossRef
10.
Clauset, A., Newman, M. E. J., & Moore, C. (2004). Finding community structure in very large networks. Physical Review E, 70, 066111.CrossRef
11.
Dellarocas, C. (2006). Strategic manipulation of internet opinion forums: Implications for consumers and firms. Management Science, 52(10), 1577–1593.CrossRef
12.
Dellarocas, C., Awad, N., & Zhang, X. M. (2005). Using online reviews as a proxy of word-of-mouth for motion picture revenue forecasting. MIT Sloan Research Paper.
13.
Denrell, J. (2008). Indirect social influence. Science, 321(5885), 47–48.CrossRef
14.
Duan, W., Gu, B., & Whinston, A. B. (2008). Do online reviews matter?—An empirical investigation of panel data. Decision Support Systems, 45(4), 1007–1016.CrossRef
15.
Eno, J., & Thompson, C. W. (2008). Generating synthetic data to match data mining patterns. Internet Computing, 12(3), 78–82.CrossRef
16.
Forman, C., Ghose, A., & Wiesenfeld, B. (2008). Examining the relation-ship between reviews and sales: The role of reviewer identify disclosure in electronic markets. Information Systems Research, 19, 291–313.CrossRef
17.
Girvan, M., & Newman, M. E. J. (2002). Community structure in social and biological networks. Proceedings of the National Academy of Sciences of the United States of America, 99(12), 7821–7826.CrossRef
18.
Hagedorn, B. A., Ciaramita, M., & Atserias, J. (2007). World knowledge in broad-coverage information filtering. In A. P. V. W. Kraaij (Ed.), Proceedings of the 30th annual international ACM SIGIR conference on research and development in information retrieval, Amsterdam, pp. 801–802.
19.
Hatzivassiloglou, V., & Wiebe, J. M. (2000). Effects of adjective orientation and gradability on sentence subjectivity. In M. Kay (Ed.), Proceedings of the 18th conference on computational linguistics, Saarbrücken, July 31–August 4, pp. 299–305.
20.
Hoag, J. E., & Thompson, C. W. (2007). A parallel general-purpose synthetic data generator. ACM SIGMOD Record, 36(1), 19–24.CrossRef
21.
Hu, M., & Liu, B. (2004). Mining and summarizing customer reviews. In R. K. W. Kim (Ed.), Proceedings of the tenth ACM SIGKDD international conference on knowledge discovery and data mining, Seattle, WA, pp. 168–177.
22.
Karypis, G., & Kumar, V. (1998). A fast and high quality multilevel scheme for partitioning irregular graphs. SIAM Journal on Scientific Computing, 20, 359–392.CrossRef
23.
Kudo, T., & Matsumoto, Y. (2004). A boosting algorithm for classification of semi-structured text. In D. W. D. Lin (Ed.), Proceedings of the 2004 conference on empirical methods in natural language processing, Barcelona, Spain, pp. 1–8.
24.
Kulis, B., & Dhillon, Y. G. (2007). Weighted graph cuts without eigenvectors: A multilevel approach. IEEE Transactions on Pattern Analysis and Machine Intelligence, 29(11), 1944–1957.CrossRef
25.
Lee, K. C., & Kwon, S. (2008). Online shopping recommendation mechanism and its influence on consumer decisions and behaviors: A causal map approach. Expert Systems with Applications, 35, 1567–1574.CrossRef
26.
Leighton, T., & Rao, S. (1999). Multicommodity max-flow min-cut theorems and their use in designing approximation algorithms. Journal of the ACM, 46(6), 787–832.CrossRef
27.
Leskovec, J., Lang, J., & Mahoney, M. (2010). Empirical comparison of algorithms for network community detection. In Proceedings of the 19th international conference on World Wide Web, Raleigh, NC, USA, April 26–30.
28.
Liu, B. (2006). Web data mining—Exploring hyperlinks, contents and usage data. Berlin: Springer.
29.
Liu, B., Hu, M., & Cheng, J. (2005). Opinion observer: Analyzing and comparing opinions on the Web. In T. H. A. Ellis (Ed.), Proceedings of the 14th international World Wide Web conference, Chiba, Japan, May 10–14, pp. 342–351.
30.
McAuley, J., & Leskovec, J. (2012). Learning to discover social circles in ego networks. In Advances in neural information processing systems 25 (NIPS 2012), Lake Tahoe, NV, 3–6 December.
31.
Newman, M. E. J., & Girvan, M. (2004). Finding and evaluating community structure in networks. Physical Review E, 69(2), 026113.CrossRef
32.
Pang, B., & Lee, L. (2008). Opinion mining and sentiment analysis. Foundations and Trends in Information Retrieval, 2(1–2), 1–135.CrossRef
33.
Pang, B., Lee, L., & Vaithyanathan, S. (2002). Thumbs up? Sentiment classification using machine learning techniques. In Proceedings of the conference on empirical methods in natural language processing, Philadelphia, PA, USA.
34.
Raghu, T. S., & Chen, H. (2007). Cyberinfrastructure for homeland security: Advances in information sharing, data mining, and collaboration systems. Decision Support Systems, 43(4), 1321–1323.CrossRef
35.
Ronhovde, P., & Nussinov, Z. (2010). An improved potts model applied to community detection. Physical Review E, 81, 046114.CrossRef
36.
Ruan, Y., Fuhry, D., & Parthasarathy, S. (2013). Efficient community detection in large networks using content and links. In Proceedings of the 22nd international conference on World Wide Web, Rio de Janeiro, 13–17 May, pp. 1089–1098.
37.
Singh, V. K., Mukherjee, M., & Mehta, G. K. (2011). Combining collaborative filtering and sentiment classification for improved movie recommendations. Multi-disciplinary trends in artificial intelligence (Vol. 7080). Berlin: Springer.
38.
Spielman, D., & Teng, S. H. (1996). Spectral partitioning works: Planar graphs and finite element meshes. In Proceedings of the 37th annual IEEE symposium on foundations of computer science, Burlington, VT, 14–16 October, pp. 96–107.
39.
Tibshirani, R., Walther, G., & Hastie, T. (2002). Estimating the number of clusters in a data set via the gap statistic. Journal of the Royal Statistical Society: Series B, 63(2), 411–423.CrossRef
40.
41.
Turney, P. D. (2001). Thumbs up or thumbs down?: Semantic orientation applied to unsupervised classification of reviews. In P. Isabelle (Ed.), Proceedings of the 40th annual meeting on association for computational linguistics, Philadelphia, PA, pp. 417–424.
42.
Vinh, N. X., Epps, J., & Bailey, J. (2009). Information theoretic measures for clustering comparison: Is a correction for chance necessary? In Proceedings of the 26th annual international conference on machine learning, Montreal, QC, June 14–18, pp. 1073–1080.
43.
Wiebe, J. M., Bruce, R. F., & O’Hara, T. P. (1999). Development and use of a gold-standard data set for subjectivity classifications. In Proceedings of the 37th annual meeting of the association for computational linguistics on computational linguistics, College Park, MA.
44.
Yang, J., McAuley, J., & Leskovec, J. (2013). Community detection in networks with node attributes. In Proceeding of the IEEE 13th international conference on data mining (ICDM 2013), Dallas, TX, 7–10 December.
45.
Yi, J., Nasukawa, T., Bunescu, R., & Niblack, W. (2003). Sentiment analyzer: Extracting sentiments about a given topic using natural language processing techniques. In J. Shavlik (Ed.), Proceedings of the third IEEE international conference on data mining, Melbourne, FL, pp. 427–434.
46.
Yip, Y., Cheung, W., & Ng, K. (2004). HARP: A practical projected clustering algorithm. IEEE Transactions on Knowledge and Data Engineering, 16(11), 1387–1397.CrossRef
47.
Yu, H., & Hatzivassiloglou, V. (2003). Towards answering opinion questions: Separating facts from opinions and identifying the polarity of opinion sentences. In Proceedings of the 2003 conference on empirical methods in natural language processing, Sapporo.
Metadaten
Titel
Sentiment community detection: exploring sentiments and relationships in social networks
verfasst von
Dong Wang
Jiexun Li
Kaiquan Xu
Yizhen Wu
Publikationsdatum
25.07.2016
Verlag
Springer US
Erschienen in
Electronic Commerce Research / Ausgabe 1/2017
Print ISSN: 1389-5753
Elektronische ISSN: 1572-9362
DOI
https://doi.org/10.1007/s10660-016-9233-8

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