14. The method according to claim 10 , wherein the taste profile includes multiple names or titles, and wherein the sparse vector is constructed from terms in the data set corresponding to all names or titles in the taste profile.
10. A method for predicting data, comprising: obtaining a name or title from a taste profile; indexing into a data set based on the name or the title, and retrieve a set of descriptive terms and corresponding term weights associated with the name or the title; constructing a sparse vector based on the set of descriptive terms and term weights; identifying a target brand or segment of interest; generating a first list of accounts who follow the brand or segment of interest by examining social media data, and a second list of additional entities followed by accounts in the first list; filtering the second list through a space mapping that maps entities to names or titles from taste profiles, to generate a subset of test data having a correspondence to the target brand or segment of interest; inputting the sparse vector to a training model including target data, wherein the target data includes the subset of test data having a correspondence to the target brand or segment of interest and the training model is based on a machine learning from ground truths from a selection of the target data, and outputting a respective binary value and confidence level for each descriptive term above a threshold, corresponding to an association between the descriptive term and the target brand or segment of interest.