ASReview uses state-of-the-art active learning techniques to solve one of the most interesting challenges in screening large amounts of text: there’s not enough time to read everything!
With the emergence of online publishing, the number of scientific papers and policy reports on any topic is skyrocketing. Simultaneously, the public press and social media also produce data by the second. Suppose you are writing a systematic review or meta-analysis, updating a medical guideline, developing evidence-based policy, or scouting for new technologies. In that case, you need to systematically search for potentially relevant documents to provide a comprehensive overview. ASReview, developed at Utrecht University, helps scholars and practitioners to get an overview of the most relevant records for their work as efficiently as possible while being transparent in the process. It allows multiple machine learning models, and ships with exploration and simulation modes, which are especially useful for comparing and designing algorithms. Furthermore, it is intended to be easily extensible, allowing third parties to add modules that enhance the pipeline with new models, data, and other extensions.