Text mining is a subdivision of data mining technologies used to extract useful information from unstructured textual data. In recent years, power distribution networks have become more complex due to the versatile consumer demand and integration of distributed energy resources. This has led to the need for enhanced data processing and analysis, i.e., data analytics, in distribution system studies. This paper for the first time explores the feasibility of application of text mining methods as a part of power system data analytics. The focus is on identifying and describing the steps that need to be taken for the knowledge extraction from large offline textual document collections and on demonstrating the effectiveness of the whole process if undertaken by a power system engineer, i.e., a nonspecialist in the area of text mining.