1. A system for predicting data, comprising: a processor configured to: obtain a name or title from a taste profile; index 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; construct a sparse vector based on the set of descriptive terms and term weights; identify a target brand or segment of interest; generate 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; filter 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; input 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 output 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.