\csq@thequote@oinit\csq@thequote@oopenDid I Say Something Wrong?\csq@thequote@ocloseA Word-Level Analysis of Wikipedia Articles for Deletion Discussions
Table 3: This table shows the performance of the SVM, NB and LM classifiers using the independent posts approach. Function words classifications are marked with “FW”. All others were full text classifications. The plus and minus symbols indicate whether the performance metric was calculated for disruptive (+) or constructive contributions (−).
['[BOLD] Classifier', '[BOLD] Recall+', '[BOLD] Recall−', '[BOLD] Precision+', '[BOLD] Precision−', '[BOLD] F1+', '[BOLD] F1−', '[BOLD] Accuracy', '[BOLD] AUC']
Overall, the support vector machine performs best, the naïve Bayes classifier ranks second and the language model ranks last. The SVM is outperformed only in negative recall by the NB classifier. However, due to lower negative precision, the NB classifier results in a worse negative F1 score than the SVM. The SVM is significantly better in positive recall and AUC, whereas the NB classifier and the LM classifier repeatedly perform similarly. When looking at the positive and negative F1 scores, it can be seen that all classifiers are better in predicting constructive posts than disruptive ones. It was expected that the SVM would outperform the NB classifier. Yet, we imagined that the language model would perform better. All in all, the three classifiers perform hardly well enough to draw reliable conclusions from the results. F1, AUC and accuracy scores of about 80% and above would be needed.