Text Analytics for Predicting Question Acceptance Rates

Online community question answering (CQA) services have gained unprecedented popularity among users wanting to voluntarily exchange solutions without a fee. However, CQA faces two challenges: the growing volume of databases and the increasing number of questions left unanswered. This article proposes classification in text analytics as one way to predict how likely a posted question is to be answered. This involves evaluating the features that characterize the question to understand why community members are or aren't answering it. Insights from text analytics could help CQA managers guide users regarding posting etiquette, thereby retaining such services' appeal and ensuring healthy knowledge growth. This study presents a feasible solution to tackle these two problems in CQA, and does so with promising results--particularly in classification by data stream mining with accelerated swarm search feature selection.