Employing Relation between Reading and Writing Skills on Age Based Categorization of Short Estonian Texts

In this paper, we present results of our study on age-based categorization of short texts as 85 words per author. We introduce a novel set of features that will reliably work with short texts, and is easy to extract from the text itself without any outside databases. These features were formerly known as variables in readability formulas. We tested datasets presented two age groups children and teens up to age 15 and adults 20 years and older. Besides readability features, we also tested widely used n-gram features. Models trained on readability features performed better or as well as models trained on n-gram features. Model generated by Support Vector Machine with readability features yield to f-score 0.953.