14. A computerized method of determining product problems from field service logs, comprising: collecting and filtering terms from a plurality of service logs; identifying a term-frequency vector for each of the logs; identifying a term-frequency matrix using the term-frequency vector, wherein rows of the term-frequency matrix comprise values for the term-frequency vectors; projecting the term-frequency matrix onto a lower dimensional space using latent semantic analysis, to create a transformed term matrix; determining a correlation matrix comprising columns and rows corresponding to terms of the transformed term matrix, and a plurality of elements each having a correlation value indicating a statistical relationship exclusively between two of the terms; converting the correlation values to distance values; creating a concept graph of connected components using the distance values that are greater than or less than a concept threshold, where each connected component comprises a concept set of terms that corresponds to a concept; clustering documents that contain terms of the concept set together; ranking logs in the documents that were clustered by frequency of occurrence of terms in the concept set.
14. A computerized method of determining product problems from field service logs, comprising: collecting and filtering terms from a plurality of service logs; identifying a term-frequency vector for each of the logs; identifying a term-frequency matrix using the term-frequency vector, wherein rows of the term-frequency matrix comprise values for the term-frequency vectors; projecting the term-frequency matrix onto a lower dimensional space using latent semantic analysis, to create a transformed term matrix; determining a correlation matrix comprising columns and rows corresponding to terms of the transformed term matrix, and a plurality of elements each having a correlation value indicating a statistical relationship exclusively between two of the terms; converting the correlation values to distance values; creating a concept graph of connected components using the distance values that are greater than or less than a concept threshold, where each connected component comprises a concept set of terms that corresponds to a concept; clustering documents that contain terms of the concept set together; ranking logs in the documents that were clustered by frequency of occurrence of terms in the concept set. 15. The method of claim 14 , further comprising identifying one or more logs that relate to a product problem based on the ranking of the log or logs.