An Optimal Feature Set for Stylometry-based Style Change detection at Document and Sentence Level

Writing style change detection models focus on determining the number of authors of documents with or without known authors. Determining the exact number of authors contributing in writing a document particularly when the authors contribute short texts in form of a sentence is still challenging because of the lack of standardized feature sets able to discriminate between the works of authors. Therefore, the task of identifying the best feature set for all the tasks of the writing style change detection is still considered important. This paper sought to determine the best feature set for the writing style change detection tasks; separating documents with several style changes (multi-authorship) from documents without any style changes (single-authorship), and determining the number and location of style changes in the case of multi-authorship. We performed exploratory research on existing stylometric features to determine the best document level and sentence level features. Document level features were extracted and used to separate single authored from multi-authored documents, while sentence level features were used to answer the question of determining the number of style changes To answer this question, we trained a random forest classifier to rank document level features and sentence level features separately, and applied an ablation test on the top 15 sentence level features using k-means clustering algorithm to confirm the effect of these features on model performance. The study found out that the best document level feature set for separating documents with and without style change was provided by an ensemble of features including number of sentence repetitions (num_sentence_repetitions) as the most determinant feature, 5-grams, 4-grams, Special_character, sentence_begin_lower, sentence_begin_upper, diversity, automated_readability_index, parenthesis_count, first_word_uppercase, lensear_write_formula, dale_chall_readability, difficult_words, type_token_ratio. These were the top ranked features in experiment one. On the other hand, the top fifteen sentence level features based on feature ranks using random forest classifier were diversity, dale_chall_readability grade, check_available_vowel, flesch_kincaid grade, parenthesis_count, colon_count, verbs, bigrams, alphabets, personal pronouns, coordinating conjunctions, interjections, modals, type_token ratio and punctuations_count. Consequently, the optimal feature set for determining the number of style changes in documents was considered based on the results of the ablation study on the top fifteen sentence level features, and was provided by an ensemble of features including personal pronouns, check_available_vowels, punctuations_counts, parenthesis count, coordinating conjunctions and colon count.