1 |
Hong, E. S., "Earlv Software Oualitv Prediction Using Support Vector Machine," Journal of Information Technology Services, Vol.10, No.12(2011), 235-245.
|
2 |
Lee, H. W. and H. C. Ahn, "An Intelligent Intrusion Detection Model Based on Support Vector Machines and the Classification Threshold Optimization for Considering the Asymmetric Error Cost," Journal of Intelligence and Information Systems, Vol.17, No.4(2011), 157-173.
|
3 |
Lee, S. J., J. Y. Choeh and J. H. Choi, "The Determinant Factors Affecting Economic Impact, Helpfulness, and Helpfulness Votes of Online," Journal of Information Technology Services, Vol.13, No.1(2014), 43-55.
|
4 |
Liu, Y., X. Huang, A. An and X. Yu, "Modeling andPredicting the Helpfulness of Online Reviews," Proceedings of the Eighth IEEE International Conference on Data Mining (2008), 443-452.
|
5 |
McAuley, J., C. Targett, J. Shi and A. van den Hengel, "Image-based recommendations onstyles and substitutes," Proceedings of the 38th International ACM SIGIR Conference on Research and Development in Information Retrieval (2015), 43-52.
|
6 |
McAuley, J., R. Pandey and J. Leskovec, "Inferringnetworks of substitutable and complementary products," Proceedings of the 21th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining (2015), 785-794.
|
7 |
Naji, I., "10 Tips to Improve your TextClassification Algorithm Accuracy and Performance," Accessed at http://thinknook.com/10-ways-to-improve-your-classification-algorithm-performance-2013-01-21/
|
8 |
Pak, A. and P. Paroubek, "Twitter as a Corpus for Sentiment Analysis and Opinion Mining," Proceedings of the Seventh conference on International Language Resources and Evaluation (LREC '10)(2010).
|
9 |
Park, S. C., S. W. Kim and H. S. Choi, "Selection Model of System Trading Strategies using SVM," Journal of Intelligence and Information Systems, Vol.20, No.2(2014), 59-71.
|
10 |
Perkins, J., Python 3 Text Processing with NLTK 3Cookbook, Packt Publishing, 2014.
|
11 |
Zhang, R. and T. Tran, "An Information gain-basedapproach for recommending useful product reviews," Knowledge and Information Systems, Vol.26, No.3(2011), 419-434.
DOI
|
12 |
Meyer, D., E. Dimitriadou, K. Hornik, A. Weingessel and F. Leisch, "e1071: Misc Functionsof the Department of Statistics, Probability Theory Group (Formerly: E1071)," TUWien. R package version 1.6-7. https://CRAN.R-project.org/package=e1071, 2015.
|
13 |
Dellarocas, C., G. Gao and R. Narayan, "Are consumers more likelyto contribute online reviews for hit or niche products?," Journal of Management Information Systems, Vol.27, No.2(2010), 127-157.
DOI
|
14 |
Cao, Q., W. Duan and Q. Gan, "Exploring determinants of voting for the 'helpfulness' online userreviews: A text mining approach," Decision Support Systems, Vol.50, No.2(2011), 511-521.
DOI
|
15 |
Choeh, J. Y., H. J. Lee and S. J. Park, "A Personalized Approach for Recommending Useful Product Reviews Basedon Information Gain," KSII Transactions on Internet and Information Systems, Vol.9, No.5(2015), 1702-1716.
DOI
|
16 |
Cruz, R. A. and H. J. Lee, "The Effects of Sentiment and Readability on Useful Votes for Customer Reviews with Count Type Review Usefulness Index," Journal of Intelligence and Information Systems, Vol.22, No.1(2016), 43-61.
DOI
|
17 |
David, S. and T. Pinch, "Six Degrees of Reputation: The Use and Abuse of Online Review and Recommendation Systems," First Monday, Vol.11, No.3(2006), Available at http://dx.doi.org/10.5210/fm.v11i3.1315 (Downloaded 15 September, 2016)
DOI
|
18 |
Dellarocas, C., "The Digitization of Word of Mouth: Promise and Challenges of Online Feedback Mechanisms," Management Science, Vol.49, No.10(2003), 1407-1424.
DOI
|
19 |
Feinerer, I., K. Hornik and D. Meyer, "TextMining Infrastructure in R," Journal of Statistical Software, Vol.25, No.5(2008), 1-54.
|