Abstract
Peoples’ perceptions of reality are conditioned on how others see the world. Unfortunately, with the vast amount of information made available through online media, such as microblog sites, it is impossible for people to absorb all information in a timely manner. Therefore, the detection of hot topics on a microblog platform is becoming increasingly important. The present paper proposes a new hot-topic detection and extraction approach based on language and topic models, which analyzes the differences in emotion distribution language models between adjacent time intervals to detect hot topics. According to the contents and repost degree of microblogs, we estimate the importance of each microblog and generate topic models. Experiments conducted on the Sina Microblog show that the proposed approach can detect and extract hot topics effectively and can thus assist the Sina Microblog platform in managing and monitoring hot topics.





Similar content being viewed by others
Explore related subjects
Discover the latest articles, news and stories from top researchers in related subjects.References
Rochat P. Early social cognition: understanding others in the first months of life. London: Psychology Press; 2014. p. 2014.
Kwak H, Lee C, Park H, Moon S. What is twitter, a social network or a news media? In: 27th World Wide Web. In: Proceedings of the 19th international conference on World Wide Web. 2010. p. 591–600.
Weng J, Lim E, Jiang J, He Q. TwitterRank: finding topic sensitive influential twitterers. In: Proceedings of the third ACM international conference on Web search and data mining. 2010. p. 261–70.
Marchetti-Bowick M, Chambers N. Learning for microblogs with distant supervision: political forecasting with Twitter. In: Proceedings of the 13th conference of the European Chapter of the Association for Computational Linguistics. 2012. p. 603–12.
Bollen J, Mao H, Zeng X. Twitter mood predicts the stock market. J Comput Sci. 2011;2(1):1–8.
Hsu C, Liu C, Lee Y. Effect of commitment and trust towards micro-blogs on consumer behavioral intention: a relationship marketing perspective. Int J Electron Bus Manag. 2010;8(4):292–303.
Yu H, Zhang Y, Liu T, Li S. Topic detection and tracking review. J Chin Inf Process. 2007;21(6):71–87.
Li B, Yu S. Research on topic detection and tracking. Comput Eng Appl. 2003;17(1):133–6.
Ku L, Liang Y. Opinion extraction, summarization and tracking in news and blog corpora. In: AAAI spring symposium: computational approaches to analyzing weblogs. 2006. p. 100–7.
Akcora C, Bayir M, Demirbas M, Ferhaosmanoglu H. Identifying breakpoints in public opinion. In: Proceedings of the first workshop on social media analytics. 2010. p. 62–6.
Cambria E, Hussain A. Sentic computing: a common-sense-based framework for concept-level sentiment analysis. Cham: Springer; 2015.
Cambria E, Schuller B, Xia Y, Havasi C. Knowledge-based approaches to concept-level sentiment analysis. IEEE Intell Syst. 2013;28(2):12–4.
Pang B, Lee L. Thumbs up? Sentiment classification using machine learning techniques. In: Proceedings of the ACL-02 conference on Empirical methods in natural language processing. 2002. p. 79–86.
Pang B, Lee L. A sentimental education: sentiment analysis using subjective summarization based on minimum cuts. In: Proceedings of the 42nd annual meeting on association for computational linguistics. 2004. p. 271–8.
Pandarachalil R, Sendhilkumar S, Mahalakshmi GS. Twitter sentiment analysis for large-scale data: an unsupervised approach. Cognit Comput. 2015;7:254–62.
Cambria E, Livingstone A, Hussain A. The hourglass of emotions. Cognitive Behavioral Systems (LNCS 7403). 2012, p. 144–57.
Wang QF, Cambria E, Liu CL, Hussain A. Common sense knowledge for handwritten Chinese recognition. Cognit Comput. 2013;5(2):234–42.
Chen Y, Zhou Q, Luo W, Du J. Classification of Chinese texts based on recognition of semantic topics. Cognit Comput. 2013;1–11.
Xu L, Lin H, Pan Y, Ren H, Chen J. Constructing the affective lexicon ontology. J China Soc Sci Tech Inf. 2008;27(2):180–5.
Lafferty J, McCallum A, Pereira F. Conditional random fields: probabilistic models for segmenting and labeling sequence data. In: 18th International conference on machine learning. 2001. p. 282–9.
Wang G. A dictionary of Chinese praise and blame words. Beijing: Encyclopedia of China Publishing House; 2001.
Zheng H, Meng Q. A dictionary of Chinese adjective. Beijing: The Commercial Press; 2004.
Cheng Z. A dictionary of Chinese idiomatic phrases. Beijing: Encyclopedia of China Publishing House; 2003.
Yang X. A dictionary of Chinese idiom. Chengdu: SiChuan Lexicographical Publishing House; 2005.
Wang J. New century dictionary of Chinese new words. Shanghai: Great Chinese dictionary Press; 2006.
Dong D. A Chinese classified dictionary. Shanghai: Great Chinese dictionary Press; 1998.
HowNet. http://www.keenage.com/.
WordNet. http://wordnet.princeton.edu/.
Zhai C, Lafferty J. A study of smoothing methods for language models applied to information retrieval. Trans Inf Syst. 2004;22(2):180–216.
Blei D, Ng A, Jordan M. Latent Dirichlet allocation. J Mach Learn Res. 2003;2003(3):993–1022.
Stuart G, Donald G. Stochastic relaxation Gibbs distributions and the Bayesian restoration of images. Pattern Anal Mach Intell IEEE Trans. 1984;6:721–41.
Lavrenko V, Croft W. Relevance-based language model. In: Proceedings of the 24th annual international ACM SIGIR conference on research and development in information retrieval. 2001. p. 120–7.
Liu X, Croft W. Cluster-based retrieval using language models. In: Proceedings of the 27th annual international ACM SIGIR conference on research and development in information retrieval. 2004. p. 186–93.
Funding
This work is partially supported by grant from the Natural Science Foundation of China (Nos. 61402075, 61572102, 61277370), Natural Science Foundation of Liaoning Province, China (Nos. 201202031, 2014020003), and State Education Ministry and The Research Fund for the Doctoral Program of Higher Education (No. 20090041110002).
Author information
Authors and Affiliations
Corresponding author
Ethics declarations
Conflict of Interest
Liang Yang, Hongfei Lin, Yuan Lin, and Shengbo Liu declare that they have no conflict of interest.
Informed Consent
All procedures followed were in accordance with the ethical standards of the responsible committee on human experimentation (institutional and national) and with the Helsinki Declaration of 1975, as revised in 2008 (5). Additional informed consent was obtained from all patients for which identifying information is included in this article.
Human and Animal Rights
This article does not contain any studies with human participants or animals performed by any of the authors.
Rights and permissions
About this article
Cite this article
Yang, L., Lin, H., Lin, Y. et al. Detection and Extraction of Hot Topics on Chinese Microblogs. Cogn Comput 8, 577–586 (2016). https://doi.org/10.1007/s12559-015-9380-6
Received:
Accepted:
Published:
Issue Date:
DOI: https://doi.org/10.1007/s12559-015-9380-6