A Simple Approach to Classify Fictional and Non-Fictional Genres

Abstract

In this work, we deploy a logistic regression classifier to ascertain whether a given document belongs to the fiction or non-fiction genre. For genre identification, previous work had proposed three classes of features, viz., low-level (character-level and token counts), high-level (lexical and syntactic information) and derived features (type-token ratio, average word length or average sentence length). Using the Recursive feature elimination with cross-validation (RFECV) algorithm, we perform feature selection experiments on an exhaustive set of nineteen features (belonging to all the classes mentioned above) extracted from Brown corpus text. As a result, two simple features viz., the ratio of the number of adverbs to adjectives and the number of adjectives to pronouns turn out to be the most significant. Subsequently, our classification experiments aimed towards genre identification of documents from the Brown and Baby BNC corpora demonstrate that the performance of a classifier containing just the two aforementioned features is at par with that of a classifier containing the exhaustive feature set.

Approximate Entropy in Canonical and Non-Canonical Fiction

“…In the context of register classification, the discriminatory power of pronouns is plausible. Qureshi et al [ 70 ] found that the ratio of the number of adjectives to the number of pronouns is a good discriminator for distinguishing fictional from non-fictional texts.…”

Leveraging Machine Learning to Explain the Nature of Written Genres

“…A distinction between fiction and non-fiction as genres to classify can be found in [21]. The former genre would include news, reviews or hobbies while the second one, fiction or adventure.…”