Labelled network motifs reveal stylistic subtleties in written texts
Table 2: Accuracy rate (%) in discriminating the authorship of books in Dataset 1. The best result obtained with the proposed technique surpasses by 15 percentage points the best performance obtained with traditional features based on the frequency of function words.
['[BOLD] Features', '| [ITALIC] W|', '[BOLD] J48', '[BOLD] kNN', '[BOLD] SVM', '[BOLD] Bayes']
The best results were obtained with the SVM, in general. For this reason, the discussion here is focused on the results obtained by this classifier. We note that, when comparing both versions of the proposed technique for the same |W|, the second version yielded best results, which reinforces the importance of function words in specific nodes. The relevance of using the local structure becomes evident when we analyze the results obtained with frequency features. While the best performance of the proposed technique reached 80% of accuracy, the best performance obtained with frequency features was only 65%. Therefore, we may expect that larger values of |W| could yield better classification accuracies, with a corresponding loss in temporal efficiency. To further probe the correlation between accuracy and the value of the parameter |W|, we evaluated the performance of the same authorship attribution task for 1≤|W|≤40. Considering the best scenario for each classifier, the SVM still outperforms all other methods. However, we did not observe an improvement in the discrimination, as the SVM does not benefit much from the addition of features. Conversely, the kNN is much benefited from the inclusion of new features. This behavior is markedly visible in the LMV2 variation, with optimized results leading to a performance similar to that obtained with the SVM. Based on these results and considering the efficiency loss associated with the inclusion of features, we used at most |W|=20 in most of the remaining experiments.