Analyzing Helpfulness of Online Reviews for User Requirements Elicitation

Online reviews have become a novel data resource for requirements elicitation.However,the quality differences of reviews cause great difficulty in eliciting highly accurate and reliable user requirements by automatic mining technology.The prerequisite of user requirements elicitation is how to discover more helpful reviews that can describe user requirements accurately.For this problem,this paper proposes an approach to analyzing the helpfulness of a review for requirements elicitation based on Complex Network Theory.In our approach,we employ the semantic association between the reviews to analyze the degree to which a review is helpful for identifying user requirements,and then discover the reviews that can describe user requirements accurately.Our approach regards the reviews as a network topology with associated content,and uses the node importance of a review in the network to measure the helpfulness of the review.The node importance of a review is computed based on topological potential by integrating the users' subjective evaluation and the objective influence of the network topology.Experimental results show that our approach can identify more helpful reviews to support user requirements elicitation with sufficient accuracy and coverage.