Process mining is a relatively new discipline that focuses on gaining process-centric knowledge from event logs collected by enterprise systems. From an academic standpoint, there has been a constant effort to develop various techniques to automatically discover process models, analyze the compliance of real-life processes to the process models, predict operational frictions, and recommend possible actions to mitigate emerging risks. As far as applications are concerned, process mining techniques have been adopted in various industries, such as healthcare, manufacturing, logistics, and finance. In this work, we analyze the process mining literature in-depth using text mining and machine learning techniques. More in detail, we (1) analyze the main research fields in process mining and their trends, (2) investigate the relationship between the fields, and (3) predict the expansion of the fields in the near future. To that end, we analyze 2,677 process mining articles from 2003 to 2022 using a range of techniques such as topic modeling with the pre-trained language model, exploratory bibliometric analysis, network and community detection, and future prediction.