1. A method comprising: defining, by a computing device, information for a set of genomes, the set of genomes describing characteristics of media programs; defining, by the computing device, which genomes in the set of genomes correspond to which topics in a set of topics; inputting, by the computing device, textual information for a plurality of media programs and the information for the set of genomes into a model; training, by the computing device, the model to determine a probability distribution of terms for the set of topics based on analyzing the textual information and the information for the set of genomes, wherein training comprises incorporation a parameter that prevents topics from being submerged by other topics that are larger via a first item in the model; and outputting, by the computing device, the trained model, wherein the probability distribution of terms is usable to determine genomes for each of the plurality of media programs, wherein a genome corresponds to a topic and is associated with a media program based on terms found in the textual information for the media program and the probability distribution of terms for the topic corresponding to the genome.