Analysis of topics in storytime books based on text mining: Preliminary findings

Abstract

This poster presents some preliminary findings from an on‐going study that explores the content of storytime books using text mining procedures. We analyzed 429 books recommended for public library storytime programs. For each, we collected two sources of text from the WorldCat database: abstracts and subject terms. Multiple textual analysis methods were employed, including term frequency, bi‐grams, term co‐occurrences, LDA topic modeling, and sentiment analysis. The preliminary findings identified different topics and genres covered in storytime books. The study found educational elements in storytime books, such as alphabet, numbers, and colors. In addition, books in storytimes involve various aspects of sentiment.