Language Use Matters: Analysis of the Linguistic Structure of Question Texts Can Characterize Answerability in Quora
Table 3: Performance of various methods (K = 10, 20, 30). First 5 lines for t = 1 month and last 5 lines for t = 3 months
['Method', '[ITALIC] K', 'Accuracy', 'Precision', 'Recall', 'F-Score', 'ROC Area']
In the prediction task, we remove all the questions posted by the anonymous users. We perform a 10-fold cross-validation with SVM classifier and achieve 76.26% accuracy with high avg. precision and recall rates for t = 1 month and 68.33% for t = 3 months Logistic regression (LR) and random forest (RF) classifiers yield very similar classification performance (at t=1 month, accuracy of 75.11% and 74.42% for LR and RF respectively) although SVM performs best among them. Consequently, we report the performance of the SVM classifier in detail. We observe that the number of topics (K) of LDA does not have a significant effect on the classification results. For K=20, we achieve the best accuracy, avg. precision, recall and the area under the ROC curve. Note that, as time progresses prediction becomes harder; thanks to the rich set of features, even with three months observation period, we are able to obtain a decent prediction accuracy. We achieve ∼33% and ∼39% improvement in terms of accuracy over Yang et al. ’s and Dror et al.’s method respectively for t=1 month Our method is performing best for prediction on shorter time periods than the baselines.