METHODS AND SYSTEMS FOR A CONTENT DEVELOPMENT AND MANAGEMENT PLATFORM
The present system and method relate to an automated crawler for crawling a primary online content object and storing a set of results, a parser for parsing the stored set of results to generate a plurality of key phrases and a content corpus, a plurality of models for processing at least one of the plurality of key phrases or the content corpus, wherein the processing results in a plurality of topic clusters which arrange topics within the primary online content object around a core topic based on semantic similarity, a suggestion generator for generating a suggested topic that is similar to at least one topic among the plurality of topic clusters and for storing the suggested topic, and an application for developing a strategy for development of online presence content.
1 . A method comprising:
controlling a machine learning system to parse content crawled from content sources to populate a content cluster data store with content objects identified from the parsed content;
iteratively applying sets of weights to the content objects to create a cluster of content objects within the content cluster data store;
assigning, by a model, relevancy scores to topics within the cluster of content objects;
generating, by a suggestion generator using output from the model, a suggested topic based upon the relevancy scores; and
controlling a conversation agent to generate and provide content to a user based upon the suggested topic.
2 . The method of claim 1 , comprising:
integrating the conversation agent into a platform for automating conversions with users based upon suggested topics generated by the suggestion generator.
3 . The method of claim 1 , comprising:
utilizing, by the conversation agent, the suggested topic to engage in a conversation with the user around topics that differentiate an enterprise.
4 . The method of claim 1 , comprising:
utilizing, by the conversation agent, the suggested topic to engage in a conversation with the user around topics semantically relevant to key phrases identified from a primary online content objected crawled from the content sources.
5 . The method of claim 1 , comprising:
utilizing, by the conversation agent, the suggested topic to engage in a conversation with the user around topics that differentiate an enterprise.
6 . The method of claim 1 , comprising:
utilizing, by the conversation agent, to populate a customer chat into a user interface.
7 . The method of claim 1 , comprising:
utilizing, by the conversation agent, to populate a customer chat into a user interface by providing draft content for editing.
8 . A non-transitory computer readable storage medium having a plurality of instructions stored thereon which, when executed across one or more processors, causes at least a portion of the one or more processors to perform operations comprising:
controlling a machine learning system to parse content crawled from content sources to populate a content cluster data store with content objects identified from the parsed content;
iteratively applying sets of weights to the content objects to create a cluster of content objects within the content cluster data store;
assigning, by a model, relevancy scores to topics within the cluster of content objects;
generating, by a suggestion generator using output from the model, a suggested topic based upon the relevancy scores; and
controlling a conversation agent to generate and provide content to a user based upon the suggested topic.
9 . The non-transitory computer readable storage medium of claim 8 , comprising:
integrating the conversation agent into a platform for automating conversions with users based upon suggested topics generated by the suggestion generator.
10 . The non-transitory computer readable storage medium of claim 8 , comprising:
utilizing, by the conversation agent, the suggested topic to engage in a conversation with the user around topics that differentiate an enterprise.
11 . The non-transitory computer readable storage medium of claim 8 , comprising:
utilizing, by the conversation agent, the suggested topic to engage in a conversation with the user around topics semantically relevant to key phrases identified from a primary online content objected crawled from the content sources.
12 . The non-transitory computer readable storage medium of claim 8 , comprising:
utilizing, by the conversation agent, the suggested topic to engage in a conversation with the user around topics that differentiate an enterprise.
13 . The non-transitory computer readable storage medium of claim 8 , comprising:
utilizing, by the conversation agent, to populate a customer chat into a user interface.
14 . The non-transitory computer readable storage medium of claim 8 , comprising:
utilizing, by the conversation agent, to populate a customer chat into a user interface by providing draft content for editing.
15 . A computing system including memory storing instructions and including a processor that executes the instructions to perform operations comprising:
controlling a machine learning system to parse content crawled from content sources to populate a content cluster data store with content objects identified from the parsed content;
iteratively applying sets of weights to the content objects to create a cluster of content objects within the content cluster data store;
assigning, by a model, relevancy scores to topics within the cluster of content objects;
generating, by a suggestion generator using output from the model, a suggested topic based upon the relevancy scores; and
controlling a conversation agent to generate and provide content to a user based upon the suggested topic.
16 . The computing system of claim 15 , comprising:
integrating the conversation agent into a platform for automating conversions with users based upon suggested topics generated by the suggestion generator.
17 . The computing system of claim 15 , comprising:
utilizing, by the conversation agent, the suggested topic to engage in a conversation with the user around topics that differentiate an enterprise.
18 . The computing system of claim 15 , comprising:
utilizing, by the conversation agent, the suggested topic to engage in a conversation with the user around topics semantically relevant to key phrases identified from a primary online content objected crawled from the content sources.
19 . The computing system of claim 15 , comprising:
utilizing, by the conversation agent, the suggested topic to engage in a conversation with the user around topics that differentiate an enterprise.
20 . The computing system of claim 15 , comprising:
utilizing, by the conversation agent, to populate a customer chat into a user interface.