Abstract
Mining user intents in online interactive behavior from social media data can effectively identify users’ motives behind communication and provide valuable information to aid medical decision-making and improve services. However, it is a challenging task due to the ambiguous semantic, irregular expressions and obscure intention classification categories. In this paper, we first define user intent categories based on speech act theory. On the basis of this, we develop a novel method to further classify users’ utterances according to their pragmatic functions. First, we design topic independent features by regularizing the utterance and categorizing the textual features. Then, we build a hierarchical model based on Hidden Markov Model (HMM) [1] to mine user intents in context sequence at both sentence and microblog level. Finally, we construct a dataset of microblogs about hot topics related to the medical event by a semi-automatic method. Experimental study shows the effectiveness of our 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
References
Rabiner, L., Juang, B.: An introduction to hidden Markov models. ASSP Mag. IEEE 3(1), 4–16 (1986)
Tsur, G., Pinter, Y., Szpektor, I., Carmel, D.: Identifying web queries with question intent. In: International Conference on World Wide Web, pp. 783–793 (2016)
Yang, Y., Tang, J.: Beyond query: interactive user intention understanding. In: IEEE International Conference on Data Mining, pp. 519–528 (2015)
Fu, B., Liu, T.: Identifying consumption target in microblog based on cross social media search. J. Front. Comput. Sci. Technol. 9, 1247–1255 (2015)
Gupta, V., Varshney, D., Jhamtani, H., et al.: Identifying purchase intent from social posts. In: International Conference on Weblogs and Social Media, pp. 180–186 (2014)
Wang, J., Cong, G., Zhao, X.W., Li, X.: Mining user intents in twitter: a semi-supervised approach to inferring intent categories for microblog. In: 29th AAAI Conference on Artificial Intelligence, pp. 318–324 (2015)
Purohit, H., Dong, G., Shalin, V.L., Thirunarayan, K., Sheth, A.P.: Intent classification of short-text on social media. In: 2015 IEEE International Conference on Smart City/SocialCom/SustainCom (SmartCity), pp. 222–228 (2015)
Austin, J.L.: How to do Things with Words. Oxford University Press, London (1975)
Searle, J.R., Kiefer, F., Bierwisch, M.: Speech Act Theory and Pragmatics, vol. 10. Springer, Dordrecht (1980). doi:10.1007/978-94-009-8964-1. Synthese language library
Wu, J.: Speech acts and Chinese sentence patterns, Doctoral dissertation, East China Normal University (2006)
Han, X., Zhao, T.: Two-fold filtering for Chinese subcategorization acquisition with diathesis alternations used as heuristic information. Inst. Comput. Linguist. 11(2), 101–114 (2006)
Vanderveken, D., MacQueen, K.: Semantic analysis of English performative verbs. In: Meaning and Speech Acts, vol. 1, pp. 166–219 (1990)
Huang, C.L., Chung, C.K., Hui, N., Lin, Y.C., Seih, Y.T., Chen, W.C., Pennebaker, J.W.: The development of the Chinese linguistic inquiry and word count dictionary. Chin. J. Psychol. 54, 185–201 (2012)
Dong, Z., Dong, Q.: Introduction to HowNet (2002). http://www.keenage.com
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2017 Springer International Publishing AG
About this paper
Cite this paper
Cui, C., Mao, W., Zheng, X., Zeng, D. (2017). Mining User Intents in Online Interactions: Applying to Discussions About Medical Event on SinaWeibo Platform. In: Chen, H., Zeng, D., Karahanna, E., Bardhan, I. (eds) Smart Health. ICSH 2017. Lecture Notes in Computer Science(), vol 10347. Springer, Cham. https://doi.org/10.1007/978-3-319-67964-8_17
Download citation
DOI: https://doi.org/10.1007/978-3-319-67964-8_17
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-319-67963-1
Online ISBN: 978-3-319-67964-8
eBook Packages: Computer ScienceComputer Science (R0)