Characterizing Finnish children’s books by target age — Complementing key feature analysis with machine learning

This study applies corpus linguistic and machine learning methods to analyze 300 Finnish children’s books intended to be read between the ages of 7 and 15, and examines the linguistic characteristics and distinctiveness of texts written for particular ages. The corpus we use, the Turku Children’s Book Lexicon (TCBC), consists of three sub-corpora based on the intended reading ages of the books ( 7-8 , 9-12 , 13-15 ) and we use two different methodologies to analyze the linguistic characteristics of texts in these age groups. First, we use Key Feature Analysis (Egbert and Biber 2023) to examine the typical functional features of texts on the corpus level. Second, for examining the distinctiveness of the text classes on the level of individual texts, we use interpretable machine learning methods and Support Vector Machines (SVM) in particular. In our analysis, we use a total of 148 features. We show that the texts in each age group have distinct linguistic characteristics, with differences in phrasal and clausal complexity, morpho-lexical richness, and lexical variation. However, classifying individual texts proves quite difficult and machine learning models struggle especially with distinguishing texts of the middle age group from the others. We also show empirical evidence that traditionally used readability metrics are insufficient for characterizing text classes. This study provides crucial information for a wide range of fields applying age-specific text data, such as psycholinguistic reading studies and the development of education technologies.