Abstract
Understanding customers’ opinion and subjectivity is regarded as an important task in various domains (e.g., marketing). Particularly, with many types of social media (e.g., Twitter and FaceBook), such opinions are propagated to other users and might make a significant influence on them. In this paper, we propose a method for understanding relationship between sentiment content corresponding with its diffusion degree in Online Social Networks. Thereby, a practical system, called TweetScope, has been implemented to efficiently collect and analyze all possible tweets from customers.
The original version of this chapter was revised: The copyright line was incorrect. This has been corrected. The Erratum to this chapter is available at DOI: 10.1007/978-3-319-05939-6_37
This is a preview of subscription content, log in via an institution to check access.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
Similar content being viewed by others
Notes
- 1.
Natural Language Toolkit package can be downloaded at http://nltk.org.
- 2.
http://twitaholic.com - \(Twitterholics\) is an online service that scan Twitter a few times a day to determine who is the biggest account.
- 3.
https://dev.twitter.com/docs provides a detail description about the latest version 1.1 of Twitter API.
References
Brown, J., Broderick, A.J., Lee, N.: Word of mouth communication within online communities: conceptualizing the online social network. J. Interact. Mark. 21(3), 2–20 (2007)
Brunelli, M., Fedrizzi, M.: A fuzzy approach to social network analysis. In: Proceedings of the 2009 International Conference on Advances in Social Network Analysis and Mining, ASONAM ’09, pp. 225–230. IEEE Computer Society, Washington, DC (2009)
Dang-Xuan, L., Stieglitz, S.: Impact and diffusion of sentiment in political communication - an empirical analysis of political weblogs. In: Proceedings of the Sixth International AAAI Conference on Weblogs and Social Media, pp. 427–430. The AAAI Press, Dublin (2012)
Trung, D.N., Jung, J.J., Lee, N., Kim, J.: Thematic analysis by discovering diffusion patterns in social media: an exploratory study with tweetScope. In: Selamat, A., Nguyen, N.T., Haron, H. (eds.) ACIIDS 2013, Part II. LNCS, vol. 7803, pp. 266–274. Springer, Heidelberg (2013)
Huffaker, D.: Dimensions of leadership and social influence in online communities. Human Commun. Res. 36(4), 593–617 (2010)
Jung, J.J.: An empirical study on optimizing query transformation on semantic peer-to-peer networks. J. Intel. Fuzzy Syst. 21(3), 187–195 (2010)
Jung, J.J.: Ontology mapping composition for query transformation on distributed environments. Expert Syst. Appl. 37(12), 8401–8405 (2010)
Jung, J.J.: Reusing ontology mappings for query segmentation and routing in semantic peer-to-peer environment. Inf. Sci. 180(17), 3248–3257 (2010)
Jung, J.J.: Service chain-based business alliance formation in service-oriented architecture. Expert Syst. Appl. 38(3), 2206–2211 (2011)
Jung, J.J.: Attribute selection-based recommendation framework for short-head user group: an empirical study by MovieLens and IMDB. Expert Syst. Appl. 39(4), 4049–4054 (2012)
Jung, J.J.: Computational reputation model based on selecting consensus choices: an empirical study on semantic wiki platform. Expert Syst. Appl. 39(10), 9002–9007 (2012)
Jung, J.J.: ContextGrid: a contextual mashup-based collaborative browsing system. Inf. Syst. Front. 14(4), 953–961 (2012)
Jung, J.J.: Discovering community of lingual practice for matching multilingual tags from folksonomies. Comput. J. 55(3), 337–346 (2012)
Jung, J.J.: Evolutionary approach for semantic-based query sampling in large-scale information sources. Inf. Sci. 182(1), 30–39 (2012)
Jung, J.J.: Semantic annotation of cognitive map for knowledge sharing between heterogeneous businesses. Expert Syst. Appl. 39(5), 1245–1248 (2012)
Jung, J.J.: Semantic optimization of query transformation in a large-scale peer-to-peer network. Neurocomputing 88, 36–41 (2012)
Kim, M., Xie, L., Christen, P.: Event diffusion patterns in social media. In: Breslin, J.G., Ellison, N.B., Shanahan, J.G., Tufekci, Z. (eds.) Proceedings of the 6th International Conference on Weblogs and Social Media (ICWSM 2012). The AAAI Press, Dublin (2012)
Pham, X.H., Jung, J.J., Hwang, D.: Beating social pulse: understanding information propagation via online social tagging systems. J. Univers. Comput. Sci. 18(8), 1022–1031 (2012)
Salmeron, J.L.: Fuzzy cognitive maps for artificial emotions forecasting. Appl. Soft Comput. 12(12), 3704–3710 (2012)
Sen, S., Lerman, D.: Why are you telling me this? an examination into negative consumer reviews on the web. J. Interact. Mark. 21(4), 76–94 (2007)
Strapparava, C., Mihalcea, R.: Learning to identify emotions in text. In: Proceedings of the 2008 ACM Symposium on Applied Computing, SAC ’08, pp. 1556–1560. ACM, New York (2008)
Bird, S., Loper, E., Klein, E.: Natural Language Processing with Python. O’Reilly Media Inc., Sebastopol (2009)
Acknowledgement
This work was supported by the BK21+ Program of the National Research Foundation (NRF) of Korea.
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2014 ICST Institute for Computer Science, Social Informatics and Telecommunications Engineering
About this paper
Cite this paper
Trung, D.N., Nguyen, T.T., Jung, J.J., Choi, D. (2014). Understanding Effect of Sentiment Content Toward Information Diffusion Pattern in Online Social Networks: A Case Study on TweetScope. In: Vinh, P., Alagar, V., Vassev, E., Khare, A. (eds) Context-Aware Systems and Applications. ICCASA 2013. Lecture Notes of the Institute for Computer Sciences, Social Informatics and Telecommunications Engineering, vol 128. Springer, Cham. https://doi.org/10.1007/978-3-319-05939-6_34
Download citation
DOI: https://doi.org/10.1007/978-3-319-05939-6_34
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-319-05938-9
Online ISBN: 978-3-319-05939-6
eBook Packages: Computer ScienceComputer Science (R0)