Stylometric studies based on tone and word length motifs

We propose a new approach to stylometric analysis combining lexical and textual information, but without annotation or other pre-processing. In particular, our study makes use Chinese tones motifs and word length motifs automatically extracted from unannotated texts. The proposed approach is based on linked data in nature as tone and word-length information is extracted from a lexicon and mapped to the text. Support vector machine and random forest were used to establish the classification models for author differentiation. Based on comparative study of classification results of different models, we conclude that the combination of word-final tones motifs, segment-final motifs and word length motifs provides the best outcome and hence is the best model.