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Knowledge and Information Systems
Published in: Knowledge and Information Systems 3/2017

17-04-2017 | Regular Paper

Mining collective knowledge: inferring functional labels from online review for business

Authors: Feifan Fan, Wayne Xin Zhao, Ji-Rong Wen, Ge Xu, Edward Y. Chang

Published in: Knowledge and Information Systems | Issue 3/2017

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Abstract

With the increasing popularity of online e-commerce services, a large volume of online reviews have been constantly generated by users. In this paper, we propose to study the problem of inferring functional labels using online review text. Functional labels summarize and highlight the main characteristics of a business, which can serve as bridges between the consumption needs and the service functions. We consider two kinds of semantic similarities: lexical similarity and embedding similarity, which characterize the relatedness in two different perspectives. To measure the lexical similarity, we use the classic probabilistic ranking formula, i.e., BM25; to measure the embedding similarity, we propose an extended embedding model which can incorporate weak supervised information derived from review text. These two kinds of similarities compensate each other and capture the semantic relatedness in a more comprehensive way. We construct a test collection consisting of four different domains based on a Yelp dataset and consider multiple baseline methods for comparison. Extensive experiments have shown that the proposed methods are very effective.
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Footnotes
5
We assume that the information supported by review writers is important. Thus, we discard all the reviews with the rating less than three stars.
 
6
For simplicity, we will only present the objective function for a single business; it will be easy to extend to multiple businesses.
 
7
Our current model requires that the attribute values should be discretized.
 
9
A label can be considered as a phrase in this method.
 
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Metadata
Title
Mining collective knowledge: inferring functional labels from online review for business
Authors
Feifan Fan
Wayne Xin Zhao
Ji-Rong Wen
Ge Xu
Edward Y. Chang
Publication date
17-04-2017
Publisher
Springer London
Published in
Knowledge and Information Systems / Issue 3/2017
Print ISSN: 0219-1377
Electronic ISSN: 0219-3116
DOI
https://doi.org/10.1007/s10115-017-1050-4

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