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 consumptio...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.展开更多
基金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.
文摘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.
