Labelled network motifs reveal stylistic subtleties in written texts
Table 3: Accuracy rate (%) in discriminating the authorship of books in Dataset 2. The best performance was obtained when the proposed technique LMV2 was used as attribute to train the SVM classifier.
['[BOLD] Features', '| [ITALIC] W|', '[BOLD] J48', '[BOLD] kNN', '[BOLD] SVM', '[BOLD] Bayes']
The authorship attribution task was also evaluated using a different dataset comprising books from 9 authors, henceforth referred to as Dataset 2. The goal of this second experiment was to evaluate the performance of Labelled motifs in characterizing shorter pieces of text. In this dataset, each book was split in several non-overlapping partitions with 8,000 words each. Because some novels are longer than others, we selected the same number of partitions per author. The results show, for this dataset, the best classifier for all studied features is the SVM. The best accuracies occurred for |W|=20. Different from the results obtained in Dataset 1, the gain in performance provided by the proposed technique is only 1.6 percentage points. This result suggests that, in an easier authorship identification scenario, structural information plays a minor role in characterizing authors’ styles. We considered this last scenario easier for two main reasons. Therefore, the classifier was probably tested with instances very similar to others it has seen during the training phase. Second, we had fewer books per author (an average of 2.1); therefore there was less variance of writing styles per author.