Text Analytics for Predicting Question Acceptance Rates

  title={Text Analytics for Predicting Question Acceptance Rates},
  author={Simon Fong and Suzy Zhou and Luiz Abel Moutinho},
  journal={IT Professional},
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… CONTINUE READING

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2014 IEEE 15th International Symposium on Computational Intelligence and Informatics (CINTI) • 2014

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