Intention Mining is an Artificial Intelligence field used to discern users’ intentions. It is widely used nowadays across various fields like Robotics, Network Forensics, Security, Bioinformatics, Learning, Map Visualization, Information Retrieval, Games, and much more. Despite the promising research in this area, it doesn’t exist any detailed overview of the existing research contributions. In this paper, we present a systematic literature review using a comparison framework that aims to highlight the different dimensions in the domain of Intention Mining. We study 137 existing contributions in Intention Mining, their underlying purposes, and the various techniques and tools employed. We used Kitchenham’s research method, including the planning, conducting, and reporting of the review phases. Additionally, we outline a research agenda highlighting several future directions, for instance, the need to formalize the intention concept, consider heterogeneous data, or provide runtime recommendations. This work also provides tips for users of artificial intelligence across various application domains.
Elali et al. (Fri,) studied this question.