Customer interaction and experience system using emotional-semantic computing

A system for enhancing customer relations through automated analysis of the sentiment and emotion contained in direct and indirect text-based communications, comprising:

• a deep web search engine comprising at least a plurality of programming instructions stored in a memory and operating on a processor of a computer, wherein the programmable instructions, when operating on the processor, cause the processor to:
• gather indirect customer communications about products and services from online sources;
• and
• an automated speech recognition engine comprising at least a plurality of programming instructions stored in a memory and operating on a processor of a computer, wherein the programmable instructions, when operating on the processor, cause the processor to:
• receive audio data from direct and indirect customer audio or video communications;
• automatically recognize the speech contained in the audio data;
• generate a textual transcription of the speech contained in the audio data;
• and
• a sentiment and emotion analyzer comprising at least a plurality of programming instructions stored in a memory and operating on a processor of a computer, wherein the programmable instructions, when operating on the processor, cause the processor to:
• receive the text of direct and indirect customer communications from the deep web search engine, the automated speech recognition engine, emails from customers, and live chats with customers;
• create a corpus of words and phrases from current and prior text of direct and indirect customer communications by parsing and organizing natural language words and phrases and stores that information as a corpus;
• create a lexicon by assigning business-domain specific vector dimensions using machine learning algorithms to the words and phrases contained in the corpus, providing information regarding the meaning including sentiment and emotion, and other context of the words and phrases in the corpus, wherein the meanings of words and phrases are represented by the vector;
• analyze incoming text for sentiment and emotions being expressed by the customer using the corpus and lexicon by assigning probability distributions to the potential sentiments and emotional content of the text using dilated convolutional artificial neural network to generate lower-dimensional dense vectors;
• output the analysis to a scoring and display engine;
• and
• a scoring and display engine comprising at least a plurality of programming instructions stored in a memory and operating on a processor of a computer, wherein the programmable instructions, when operating on the processor, cause the processor to:
• receive analyses of sentiment and emotion in direct and indirect customer communications from the sentiment and emotion analyzer;
• cumulatively assign similarity scores to products or services of interest based on the incoming analyses that includes relative distance of the vectors;
• and
• display the results of the scoring in textual, tabular, or graphical format to make business decisions.