1. A computer-implemented method comprising: using a processor to mine user related content information, wherein the information is mined from an information repository; filtering the mined user related content information from the information repository, wherein the filtering comprises identifying a subset of the mined user related content information comprising information related to a predetermined category; identifying, using a cosine similarity measure, a plurality of words having a similarity to a seed set of words by analyzing the subset, using a plurality of analyzers, wherein each analyzer is configured to capture a plurality of representational variations, from the information repository, related to the seed set of words; classifying, based on the analyzing, the plurality of representational variations, wherein the classifying comprises ranking the filtered user related content information; combining the classified plurality of representational variations of the user related content information from each of the plurality of analyzers, wherein the combining comprises identifying a relevancy of a representational variation to a user intent based upon the ranking of the filtered user related content information; and training a classifier for characterizing real-time intention content from information repositories using the combined plurality of representational variations.
2. The method as claimed in claim 1 , further comprising pre-processing the user related content information mined from an information repository prior to said analyzing.