1. A method comprising: generating, via a first topic model computer, a first term vector identifying a first topic in a plurality of documents in a document corpus; generating, via a second topic model computer, a second term vector identifying a second topic in the plurality of documents in the document corpus; linking, via a topic detection computer, each of the first and second topics across the plurality of documents in the document corpus, wherein linking comprises matching of the each of the first and second topics across the plurality of documents in the document corpus and indicates a tag associated with metadata that the first and second topics are each identified in at least one document in the document corpus; assigning, via the topic detection computer, a relatedness score weight to each of the linked first and second topics based on co-occurrence of each of the linked first and second topics across the plurality of documents in the document corpus; determining, via the topic detection computer, whether the first and second linked topics are related across the plurality of documents in the document corpus based at least in part on the relatedness score weight; executing via the first topic model computer, a master topic computer model based on a multi-component extension of latent Dirichlet allocation having a first set of model parameters; and executing via the second topic model computer, a periodic new topic computer model based on the multi-component extension of latent Dirichlet allocation having a second set of model parameters different from the first set of model parameters.
1. A method comprising: generating, via a first topic model computer, a first term vector identifying a first topic in a plurality of documents in a document corpus; generating, via a second topic model computer, a second term vector identifying a second topic in the plurality of documents in the document corpus; linking, via a topic detection computer, each of the first and second topics across the plurality of documents in the document corpus, wherein linking comprises matching of the each of the first and second topics across the plurality of documents in the document corpus and indicates a tag associated with metadata that the first and second topics are each identified in at least one document in the document corpus; assigning, via the topic detection computer, a relatedness score weight to each of the linked first and second topics based on co-occurrence of each of the linked first and second topics across the plurality of documents in the document corpus; determining, via the topic detection computer, whether the first and second linked topics are related across the plurality of documents in the document corpus based at least in part on the relatedness score weight; executing via the first topic model computer, a master topic computer model based on a multi-component extension of latent Dirichlet allocation having a first set of model parameters; and executing via the second topic model computer, a periodic new topic computer model based on the multi-component extension of latent Dirichlet allocation having a second set of model parameters different from the first set of model parameters. 5. The method of claim 1 , wherein the linking step further comprising generating a graphical representation of co-occurring linked topics across the plurality of documents in the document corpus.