On the role of words in the network structure of texts: application to authorship attribution
Table 1: Accuracy rates (in percentage) in identifying the authors in the three collections, using several machine learning algorithms. Results are shown with the input comprising the whole distance matrices (without MDS) and applying MDS on the matrices. For the three last algorithms MDS improved accuracy in all cases.
['[EMPTY]', 'J48', 'KNN', 'NB', 'RBFN']
The approach based on distance matrices as input for the classification algorithms was applied for the three collections, for which the success score for classification by chance is 1/8=12.5%. It seems therefore that reducing the dimensionality actually amounted to an efficient feature selection, probably eliminating data that brought noise to the analysis. With MDS, typical accuracy rates were above 90% and the maximum value was 98.75% obtained with KNN for the third collection which corresponds to only one text (out of 80) not correctly classified. These scores demonstrate the added value of using the network structure over relying only on the frequency of appearance of features. Significantly, the higher scores for the approach introduced here are maintained when changing the classification algorithm (KNN, NB, and RBFN), which indicates the robustness of the proposed metrics. Also worth noting is that the present approach outperforms a previous one where the topology of networks was taken without considering the labels of the nodes (words) Akimushkin et al. In addition, the approach presented is less demanding, both computationally and conceptually, than the previous one.