Abstract
User generated content on social media has attracted much attention from service/product providers, as it contains plenty of potential commercial opportunities. However, previous work mainly focuses on user Consumption Intention (CI) identification, and little effort has been spent to mine intention-related products. In this paper, we propose a novel approach to mine intention-related products on online Question & Answer (Q&A) community. Making use of the question-answer pairs as data source, we first automatically extract candidate products based on dependency parser. And then by means of the collocation extraction model, we identify the real intention-related products from the candidate set. The experimental results on our carefully constructed evaluation dataset show that our approach achieves better performance than two natural baseline methods. Our method is general enough for domain adaptation.
This is a preview of subscription content, log in via an institution.
Buying options
Tax calculation will be finalised at checkout
Purchases are for personal use only
Learn about institutional subscriptionsPreview
Unable to display preview. Download preview PDF.
References
Ashkan, A., Clarke, C.L.: Term-based commercial intent analysis. In: Proceedings of the 32nd International ACM SIGIR Conference on Research and Development in Information Retrieval, pp. 800–801. ACM (July 2009)
Che, W., Li, Z., Liu, T.: Ltp: A Chinese language technology platform. In: Proceedings of the 23rd International Conference on Computational Linguistics: Demonstrations, pp. 13–16. Association for Computational Linguistics (August 2010)
Dai, H.K., Zhao, L., Nie, Z., Wen, J.R., Wang, L., Li, Y.: Detecting online commercial intention (OCI). In: Proceedings of the 15th International Conference on World Wide Web, pp. 829–837. ACM (May 2006)
Grinstead, C.M., Snell, J.L.: Introduction to probability. American Mathematical Soc. (1998)
Hollerit, B., Kröll, M., Strohmaier, M.: Towards linking buyers and sellers: Detecting commercial Intent on twitter. In: Proceedings of the 22nd International Conference on World Wide Web Companion, pp. 629–632. International World Wide Web Conferences Steering Committee (May 2013)
Kaufmann, M., Kalita, J.: Syntactic normalization of twitter messages. In: International Conference on Natural Language Processing, Kharagpur, India (July 2010)
Liu, Z., Wang, H., Wu, H., Li, S.: Collocation extraction using monolingual word alignment method. In: Proceedings of the 2009 Conference on Empirical Methods in Natural Language Processing, vol. 2, pp. 487–495. Association for Computational Linguistics (August 2009)
Pak, A., Paroubek, P.: Twitter as a Corpus for Sentiment Analysis and Opinion Mining. In: LREC (May 2010)
Ritter, A., Clark, S., Etzioni, O.: Named entity recognition in tweets: An experimental study. In: Proceedings of the Conference on Empirical Methods in Natural Language Processing, pp. 1524–1534. Association for Computational Linguistics (July 2011)
Shah, C., Pomerantz, J.: Evaluating and predicting answer quality in community QA. In: Proceedings of the 33rd International ACM SIGIR Conference on Research and Development in Information Retrieval, pp. 411–418. ACM (July 2010)
Sakaki, T., Okazaki, M., Matsuo, Y.: Earthquake shakes Twitter users: Real-time event detection by social sensors. In: Proceedings of the 19th International Conference on World Wide Web, pp. 851–860. ACM (April 2010)
Wang, J., Zhao, W.X., Wei, H., Yan, H., Li, X.: Mining New Business Opportunities: Identifying Trend related Products by Leveraging Commercial Intents from Microblogs. In: EMNLP, pp. 1337–1347 (2013)
Zhao, W.X., Jiang, J., He, J., Song, Y., Achananuparp, P., Lim, E.P., Li, X.: Topical keyphrase extraction from twitter. In: Proceedings of the 49th Annual Meeting of the Association for Computational Linguistics: Human Language Technologies, vol. 1, pp. 379–388. Association for Computational Linguistics (June 2011)
Author information
Authors and Affiliations
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2014 Springer-Verlag Berlin Heidelberg
About this paper
Cite this paper
Duan, J., Ding, X., Liu, T. (2014). Mining Intention-Related Products on Online Q&A Community. In: Huang, H., Liu, T., Zhang, HP., Tang, J. (eds) Social Media Processing. SMP 2014. Communications in Computer and Information Science, vol 489. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-662-45558-6_2
Download citation
DOI: https://doi.org/10.1007/978-3-662-45558-6_2
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-662-45557-9
Online ISBN: 978-3-662-45558-6
eBook Packages: Computer ScienceComputer Science (R0)