document: Pragmatic text mining : minimizing human effort to quantify many issues in call logs We discuss our experiences in analyzing customer-support issues from the unstructured free-text fields of technical-support call logs . The identification of frequent issues and their accurate quantification is essential in order to track aggregate costs broken down by issue type , to appropriately target engineering resources , and to provide the best diagnosis , support and documentation for most common issues . We present a new set of techniques for doing this efficiently on an industrial scale , without requiring manual coding of calls in the call center . Our approach involves ( 1 ) a new text clustering method to identify common and emerging issues ; ( 2 ) a method to rapidly train large numbers of categorizers in a practical , interactive manner ; and ( 3 ) a method to accurately quantify categories , even in the face of inaccurate classifications and training sets that necessarily can not match the class distribution of each new month 's data . We present our methodology and a tool we developed and deployed that uses these methods for tracking ongoing support issues and discovering emerging issues at HP .
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