The aim of this study is to provide an overview of human-in-the-loop text classification. Automated text classification faces several challenges that negatively affect its applicability in real-world domains. General obstacles are a lack of labelled examples, limited held-out accuracy, missing user trust, run-time constraints, low data quality and natural fuzziness. Human-in-the-loop is an emerging paradigm to continuously support machine processing, i.e. text classification, with prior human knowledge, aiming to overcome the limitations of purely artificial processing. In this survey, we review current challenges of pure automated text classifiers and outline how a human-in-the-loop can overcome these obstacles. We focus on end-to-end text classification and feedback of domain-experts, which do not process technical knowledge about the algorithms used. Further, we discuss common techniques to guide human attention and efforts within the text classification process.