The engineering process relies on extensive sources of knowledge in textual form, such as books, conference papers, product catalogs, and web pages. Leveraging the full value of the knowledge in textual data is difficult due to the length of time required to read and comprehend the documents. Text mining untaps the vast amounts of information buried in textual data. Text mining can process the content of large numbers of documents, semantically analyze and organize the content, and extract the useful information for downstream applications. Once analyzed, the informational content of documents can be indexed and accessed in multiple formats, such as summaries, key concepts, events, relationships, and visual representations. Applications of text mining in engineering are diverse and include predictive warranty analysis, quality improvements, patent analysis, competitive assessments, FMEA, and product searches.

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