Semantic space scanning for differential topic extraction

A method for extracting differential topics from a dataset, the method comprising:

• receiving, at a computing device, a dataset from one or more servers, wherein the dataset comprises user feedback data associated with a software program;
• extracting, via the computing device, text from the dataset;
• converting, via the computing device, the extracted text to vector data within a high-dimensional vector space;
• determining, via the computing device, anomalous data clusters associated with the vector data using statistical analysis;
• differentiating, via the computing device, overlapping anomalous data clusters using a classification algorithm, wherein the differentiated overlapping anomalous data clusters are associated with specific topics within the user feedback data;
• and
• exporting, via the computing device, each specific topic associated with the differentiated overlapping data clusters.

A system for extracting geographically differential topics from a dataset, the system comprising:

• a user interface;
• a memory for storing executable program code;
• and
• one or more electronic processors coupled to the memory and the user interface, the one or more electronic processors configured to:
• receive a dataset from one or more servers, wherein the dataset comprises user feedback data associated with a software program;
• execute a differential topic extraction algorithm to isolate relevant text within the dataset;
• extract text from the dataset;
• convert extracted text to vector data by executing a distributional semantics modeling algorithm;
• map the vector data in a high-dimensional space;
• determine anomalous data clusters associated with the vector data using a Bayesian scan statistics statistical analysis;
• differentiate overlapping anomalous data clusters using a classification algorithm, wherein the differentiated overlapping anomalous data clusters are associated with specific topics;
• and
• export each specific topic associated with the differentiated overlapping data clusters.