20. The computer-readable medium according to claim 19 , wherein the first list of accounts is generated by selecting accounts who follow the target brand or segment of interest in a random sampling fashion.
19. A non-transitory computer-readable medium having stored thereon sequences of instructions, the sequences of instructions including instructions which when executed by a computer system causes the computer system to perform a method for predicting data, the method 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 what additional entities are 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.