4. The method of claim 1 , wherein said personal information topic space comprises any of marital status, age, date of birth, travel plans, anniversary date, preferred brands, family related information, financial information, level of education, vehicles owned, location, health related information, level of familiarity with a specific area, and price consciousness.
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.