›› 2015, Vol. 30 ›› Issue (5): 1054-1062.doi: 10.1007/s11390-015-1581-7

Special Issue: Artificial Intelligence and Pattern Recognition; Data Management and Data Mining

• Special Section on Social Media Processing • Previous Articles     Next Articles

Mining Intention-Related Products on Online Q&A Community

Jun-Wen Duan(段俊文), Yi-Heng Chen(陈毅恒), Member, CCF Ting Liu*(刘挺), Senior Member, CCF, ACM, Xiao Ding(丁效), Member, CCF   

  1. School of Computer Science and Technology, Harbin Institute of Technology, Harbin 150001, China
  • Received:2014-11-17 Revised:2015-07-09 Online:2015-09-05 Published:2015-09-05
  • Contact: Ting Liu E-mail:tliu@ir.hit.edu.cn
  • About author:Jun-Wen Duan received his B.E. degree in computer science and technology from Harbin Institute of Technology, Harbin, in 2013. Currently, he is a Ph.D. candidate in Harbin Institute of Technology. His current research interests include natural language processing, social computing, and text mining.
  • Supported by:

    The research is supported by the National Basic Research 973 Program of China under Grant No. 2014CB340503 and the National Natural Science Foundation of China under Grant Nos. 61133012, 61202277, and 61472107.

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, focusing on the Baby & Child Care domain, we propose a novel approach to mine intention-related products on online question and answer (Q&A) community. Making use of the question-answering 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.

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