Abstract
In recent years, there is a rapid increased use of social networking platforms in the forms of short-text communication. Such communication can be indicative to popular public opinions and may be influential to real-life events. It is worth to identify topic groups from it automatically so it can help the analyst to understand the social network easily. However, due to the short-length of the texts used, the precise meaning and context of such texts are often ambiguous. In this paper, we proposed a hybrid framework, which adapts and extends the text clustering technique that uses Wikipedia as background knowledge. Based on this method, we are able to achieve higher level of precision in identifying the group of messages that has the similar topic.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
Notes
- 1.
MongoDB: http://www.mongodb.org/.
References
Kumar, R., Raghavan, P., Rajagopalan, S., Tomkins, A.: Trawling the web for emerging cyber-communities. In: Proceedings of the Eighth International Conference on World Wide Web, WWW ’99, pp. 1481–1493, NY, USA (1999)
Hotho, A., Hotho, A., Staab, S., Staab, S., Stumme, G., Stumme, G.: Text Clustering Based on Background Knowledge (2003)
Newman, M.E.J.: Detecting community structure in networks. Eur. Phys. J. B—Condens. Matter Complex Syst. 38(2), 321–330 (2004)
Mihalcea, R., Csomai, A.: Wikify!: linking documents to encyclopedic knowledge. In In CIKM 07: Proceedings of the Sixteenth ACM Conference on Information and Knowledge Management, pp. 233–242. ACM (2007)
Milne, D., Witten, I.H.: Learning to link with wikipedia. In: Proceedings of the 17th ACM Conference on Information and Knowledge Management, CIKM ’08, pp. 509–518, NY, USA, 2008. ACM
Kunegis, J., Lommatzsch, A., Bauckhage, C.: The slashdot zoo: mining a social network with negative edges. In: Proceedings of the 18th International Conference on World Wide Web, WWW ’09, pp. 741–750, NY, USA. ACM (2009)
Wang, P., Hu, J., Zeng, H.-J., Chen, Z.: Using wikipedia knowledge to improve text classification. Knowl. Inf. Syst. 19(3), 265–281 (2009)
Ferragina, P., Scaiella, U.: Tagme: on-the-fly annotation of short text fragments (by wikipedia entities). In: Proceedings of the 19th ACM International Conference on Information and Knowledge Management, CIKM ’10, pp. 1625–1628, NY, USA. ACM (2010)
Fujiwara, Y., Irie, G., Kitahara, T.: Fast algorithm for affinity propagation. In: Proceedings of the Twenty-Second International Joint Conference on Artificial Intelligence, vol. 3, IJCAI’11, pp. 2238–2243. AAAI Press (2011)
Navigli, R.: A quick tour of word sense disambiguation, induction and related approaches. In: Proceedings of the 38th International Conference on Current Trends in Theory and Practice of Computer Science, SOFSEM’12, pp. 115–129, Berlin, Heidelberg (2012)
Roseberg, A., Hirschberg, J.: V-Measure: A Conditional Entropy-Based External Cluster Evaluation Measure
Zhang, X.. Furtlehner, C., Sebag, M.: Distributed and incremental clustering based on weighted affinity propagation. In: Proceedings of the 2008 Conference on STAIRS 2008, pp. 199–210, Amsterdam, The Netherlands. IOS Press (2008)
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2015 Springer International Publishing Switzerland
About this paper
Cite this paper
Yang, CL., Chen-Burger, YH. (2015). A Hybrid On-line Topic Groups Mining Platform. In: Jezic, G., Howlett, R., Jain, L. (eds) Agent and Multi-Agent Systems: Technologies and Applications. Smart Innovation, Systems and Technologies, vol 38. Springer, Cham. https://doi.org/10.1007/978-3-319-19728-9_17
Download citation
DOI: https://doi.org/10.1007/978-3-319-19728-9_17
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-319-19727-2
Online ISBN: 978-3-319-19728-9
eBook Packages: EngineeringEngineering (R0)