1. A computer implemented method for performing analysis on data generated by user interactions, comprising: providing a processor executing instructions for receiving text information from at least one interaction between a user and an agent; said processor text mining said interaction information to extract personal information relating to said user automatically; said processor using a topic model to extract lines from said text information to reduce a number of dimensions required to represent the text, wherein all information of interest is highly pronounced, and wherein a resulting lower dimensional representation of the text allows significantly faster computations; said processor extracting said lines of text as anchored text lines that are indicative of personal information that is present in the anchored text lines; said processor identifying said anchored text lines by checking for specific keywords which are present when said user is mentioning personal information during said interaction; said processor applying a statistical technique to said anchored text lines to discover information present in the anchored text lines; said processor using a k nearest neighbor algorithm to discover said information present in the anchored text lines; said processor representing said text in a topic space with a score along each axis to indicate an extent to which said text contains personal information about said user; upon receiving a new data point, which optionally comprises an anchored text line from said text, said processor picking the k closest points to said new data point, determining a predominant class among classes in the k closest points, and assigning said predominant class to said new data point; said processor assigning a user corresponding to said text to at least one class based on said identified personal information; and said processor classifying said user based upon said extracted lines of text.
1. A computer implemented method for performing analysis on data generated by user interactions, comprising: providing a processor executing instructions for receiving text information from at least one interaction between a user and an agent; said processor text mining said interaction information to extract personal information relating to said user automatically; said processor using a topic model to extract lines from said text information to reduce a number of dimensions required to represent the text, wherein all information of interest is highly pronounced, and wherein a resulting lower dimensional representation of the text allows significantly faster computations; said processor extracting said lines of text as anchored text lines that are indicative of personal information that is present in the anchored text lines; said processor identifying said anchored text lines by checking for specific keywords which are present when said user is mentioning personal information during said interaction; said processor applying a statistical technique to said anchored text lines to discover information present in the anchored text lines; said processor using a k nearest neighbor algorithm to discover said information present in the anchored text lines; said processor representing said text in a topic space with a score along each axis to indicate an extent to which said text contains personal information about said user; upon receiving a new data point, which optionally comprises an anchored text line from said text, said processor picking the k closest points to said new data point, determining a predominant class among classes in the k closest points, and assigning said predominant class to said new data point; said processor assigning a user corresponding to said text to at least one class based on said identified personal information; and said processor classifying said user based upon said extracted lines of text. 3. The method of claim 1 , further comprising: said processor initially transcribing said interaction into text.