Method of automated discovery of topics relatedness

A system comprising:

• a first computer comprising a processor executing a master topic model (MTM) computer module, the first computer configured to generate a first term vector identifying a first topic in a plurality of documents in a document corpus;
• a second computer comprising a processor executing a periodic new model (PNM) computer module, the second computer configured to generate a second term vector identifying a second topic in the plurality of documents in the document corpus;
• and
• a third computer comprising a processor executing a change detection computer module, the third computer configured to:
• (a) link each of the first and second topics across the plurality of documents in the document corpus by matching the first and second topics across the plurality of documents in the document corpus, a link indicating a tag associated with metadata that the first and second topics are each identified in at least one document in the document corpus,
• (b) assign 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,
• (c) determine 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;
• (d) execute a master topic computer model based on a multi-component extension of latent Dirichlet allocation having a first set of model parameters;
• and
• the second computer's processor further executing a periodic new topic module to detect a new topic, the new topic module configuring the second computer to perform a new topic 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.

A non-transitory computer readable medium having stored thereon computer executable instructions comprising:

• generating, via a first topic model computer module of a 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 module of the computer, a second term vector identifying a second topic in the plurality of documents in the document corpus;
• linking, via a topic detection computer module of the 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 module of the 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 module of the 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 module, 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 module, 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.