Abstract
Classifying Micro-blog content is a popular research topic in social media, which can help users access their favorite information quickly. Much research focuses on classifying Micro-blog content with short text dataset. The challenge is the classification effect may be hampered by content ambiguity. To address this challenge, we propose a novel classification framework using external knowledge base and the Bayesian inference method. We first introduce Baidu Encyclopedia to extract effective features, and then we train efficient classifiers with the probabilistic graphic model. The proposed model can classify micro-blog content reliably. Experiments on Sina-weibo dataset demonstrate the effectiveness of the proposed method.
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
Preview
Unable to display preview. Download preview PDF.
References
Zeng, D., Chen, H., Lusch, R., Li, S.-H.: Social media analytics and intelligence. IEEE Intelligent Systems 25(6), 13–16 (2010)
Bakshy, E., Rosenn, I., Marlow, C., Adamic, L.: The Role of Social Networks in Information Diffusion. In: Proceedings of the 21st ACM Conference on the World Wide Web 2012, pp. 519–528. ACM (2012)
Akshay, J., Xiaodan, S., Tim, F., Belle, T.: Why We Twitter: Understanding Microblogging Usage and Communities. In: Proc. of the 9th WebKDD and 1st SNA-KDD 2007 Workshop on Web Mining and Social Network Analysis. ACM (2007)
Liu, Z., Yu, W., Chen, W., Wang, S., Wu, F.: Short text feature selection for micro-blog mining. In: International Conference on Computational Intelligence and Software Engineering (CiSE), pp. 1–4 (2010)
Banerjee, N., Chakraborty, D., Joshi, A., Mittal, S., Rai, A., Ravindran, B.: Towards Analyzing Micro-Blogs for Detection and Classification of Real-Time Intentions. In: Sixth International AAAI Conference on Weblogs and Social Media (May 2012)
Lee, K., Palsetia, D., Narayanan, R., Patwary, M., Agrawal, A., Choudhary, A.: Twitter trending topic classification. In: Proceedings of 11th International Conference on Data Mining, pp. 251–258. IEEE (December 2011)
Fan, Y., Liu, H.: Research on Chinese Short Text Classification Based on Wikipedia. New Technology of Library and Information Service 3, 47–52 (2012)
Banerjee, S., Ramanathan, K., Gupta, A.: Clustering short texts using wikipedia. In: Proceedings of the 30th Annual International ACM SIGIR Conference on Research and Development in Information Retrieval, pp. 787–788. ACM (2007)
Graepel, T., Candela, Q., Borchert, T., Herbrich, R.: Web-Scale Bayesian Click Through rate Prediction for Sponsored Search Advertising in Micro-soft Bing Search Engine. In: International Conference on Machine Learning, pp. 13–20 (2010)
Herbrich, R., Minka, T., Graepel, T.: TrueSkill: A Bayesian Skill Rating System. In: Advances in Neural Information Processing Systems 20, pp. 569–576. The MIT Press (2007)
Gao, H., Li, Y., Li, Q., Zeng, D.: The Powerful Model Adpredictor for Search Engine Switching Detection Challenge. In: Workshop on Web Search Click Data, the Sixth ACM International Conference on Web Search and Data Mining (2013)
Author information
Authors and Affiliations
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2013 Springer-Verlag Berlin Heidelberg
About this paper
Cite this paper
Gao, H., Li, Q., Zheng, X. (2013). Label Micro-blog Topics Using the Bayesian Inference Method. In: Wang, G.A., Zheng, X., Chau, M., Chen, H. (eds) Intelligence and Security Informatics. PAISI 2013. Lecture Notes in Computer Science, vol 8039. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-39693-9_3
Download citation
DOI: https://doi.org/10.1007/978-3-642-39693-9_3
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-642-39692-2
Online ISBN: 978-3-642-39693-9
eBook Packages: Computer ScienceComputer Science (R0)