Arabica: A Python package for exploratory analysis of text data

Abstract

Research meta-data is typically recorded as a time series with dimensions of cross-sections (e.g., article title, journal, volume, issue, author's names, and affiliations) and time (e.g., publication date). Meta-datasets provide valuable insights into the research trends in a particular field of science. Meta-analysis (a group of methods to analyze research meta-data) currently does not implement text analytics in either programming language. This package aims to fill that need. Arabica offers descriptive analytics, visualization, sentiment classification, and structural break analysis for exploratory analysis of research meta-datasets in easy-to-use Python implementation.