Text Analytics for Predicting Question Acceptance Rates

@article{Fong2015TextAF,
  title={Text Analytics for Predicting Question Acceptance Rates},
  author={Simon Fong and Suzy Zhou and Luiz Abel Moutinho},
  journal={IT Professional},
  year={2015},
  volume={17},
  pages={34-41}
}
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

From This Paper

Figures, tables, and topics from this paper.

References

Publications referenced by this paper.
Showing 1-10 of 18 references

Improving classification accuracy using Fuzzy Clustering Coefficients of Variations (FCCV) feature selection algorithm

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

The best answers? Think twice: Online detection of commercial campaigns in the CQA forums

2013 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining (ASONAM 2013) • 2013

Similar Papers

Loading similar papers…