Abstract: Disclosed is a system and associated methods to optimize search in order to present emerging concepts that relate to a query for an established concept. The system extracts phrases from different content, generates a first cluster with a first set of phrases that contain a first common set of words and a second cluster with a second set of phrases that contain a second common set of words. The system clusters the first cluster and the second cluster to a cluster group based on a related word amongst the first and second set of phrases. The system receives a query for a particular word in the first common set of words, selects the cluster group based on the phrases in the first cluster of the cluster group having the particular word, and presents search results based on content that includes phrases in the second cluster of the cluster group.

Abstract: Disclosed is a system and associated methods to optimize search in order to present emerging concepts that relate to a query for an established concept. The system extracts phrases from different content, generates a first cluster with a first set of phrases that contain a first common set of words and a second cluster with a second set of phrases that contain a second common set of words. The system clusters the first cluster and the second cluster to a cluster group based on a related word amongst the first and second set of phrases. The system receives a query for a particular word in the first common set of words, selects the cluster group based on the phrases in the first cluster of the cluster group having the particular word, and presents search results based on content that includes phrases in the second cluster of the cluster group.

Abstract: A quality assessment system models product or service quality based on contextual relevance. A neural network generates a contextual relevance model that differentiates descriptive characteristics based on a modeled relevance of each descriptive characteristic to the product or service. The system filters reviews based on the contextual relevance model to retain filtered reviews that reference any of the first set of descriptive characteristics. The system generates theme clusters with an encoder. Each theme cluster groups a different subset of the filtered reviews based on an amount of semantic similarity between the different subset of reviews and the theme cluster. The system presents an interface with a first visualization and a second visualization. The first visualization graphically represents a sentiment expressed in reviews grouped to the first theme cluster, and the second visualization graphically represents a sentiment expressed in reviews groups to the second theme cluster.