Demographic and media preference prediction using media content data analysis

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 terms and corresponding term weights associated with the name or the title;
• constructing a sparse vector based on the set of 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
• outputting a respective binary value and confidence level for each term, corresponding to an association between the term and the target brand or segment of interest.

A 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 terms and corresponding term weights associated with the name or the title;
• constructing a sparse vector based on the set of 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
• outputting a respective binary value and confidence level for each term, corresponding to an association between the term and the target brand or segment of interest.