featuresdiction

Featuresdiction is a framework in linguistic analysis that concentrates on the set of features that characterize diction in a text. It uses a feature-based representation of linguistic style to enable systematic comparison across texts, authors, genres, or time periods. While related to traditional style analyses, featuresdiction emphasizes quantitative feature extraction and statistical interpretation.

Key feature categories include lexical features (vocabulary richness, average word length, frequency of domain-specific terms), morpho-syntactic features (pronoun use, function words, part-of-speech patterns), syntactic features (sentence length, subordination rate, clause structure), and discourse features (cohesive devices, transitions, reference chains). Additional markers cover register and jargon, slang, archaisms, or rhetorical devices.

Extraction and analysis typically rely on natural language processing tools to compute feature vectors from texts. These vectors can feed machine learning models or statistical tests for tasks such as authorship attribution, genre classification, readability assessment, or plagiarism detection. Featuresdiction can also support comparative studies of historical texts or media content across genres.

Limitations include sensitivity to topic, domain, and text length, which can bias feature values. Cross-language applications require careful adaptation, and results may depend on preprocessing choices such as tokenization and normalization. Interpretation of features should consider contextual factors and topic-related effects.

See also: stylometry, linguistic feature engineering, natural language processing.