Customer interaction and experience system using emotional-semantic computing

1. 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.

2. The system of claim 1 wherein vector clustering is used to segment similar content.

3. The system of claim 1 wherein linear transformation is used to translate between the language of business-specific domains.

4. The system of claim 1 wherein vector algebra is used to find content needed to achieve target emotional-semantic content.

5. The system of claim 1 wherein vector calculus is used to understand and predict time evolution in emotion and meaning by calculating rates of change of vectors and time dynamics.

6. A method for enhancing customer relations through automated analysis of the sentiment and emotion contained in text-based communications, comprising:

a deep web search engine, an automated speech recognition engine, a sentiment and emotion analyzer, 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, automatically cause the processor to perform the steps of:

(a) gathering indirect customer communications about products and services from online sources;

(b) gathering direct customer communications about products and services including audio data and video data;

(c) converting any audio data in the customer communications to text using automated speech recognition to generate a textual transcription of the speech contained in the audio data;

(d) creating a corpus of words and phrases from the gathered and converted text of direct and indirect customer communications by parsing and organizing natural language words and phrases and stores that information as a corpus;

(e) creating a lexicon by assigning business-domain specific vector dimensions using machine learning algorithms to the words and phrases contained in the corpus, associating meaning of the words and phrases including sentiment and emotion depending on context, wherein the meanings of words and phrases are represented by the vector;

(f) analyzing 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;

(g) receiving the analyses of the sentiment and emotion in direct and indirect customer communications from the sentiment and emotion analyzer;

(h) outputting the analysis to a scoring and display engine; and

(i) displaying the results of the scoring in a textual, tabular, or graphical format to make business decisions.

7. The method of claim 6 comprising the additional step of using vector clustering to segment similar content.

8. The method of claim 6 comprising the additional step of using linear transformation to translate between the language of business-specific domains.

9. The method of claim 6 comprising the additional step of using vector algebra to find content needed to achieve target emotional-semantic content.

10. The method of claim 6 comprising the additional step of using vector calculus to understand and predict time evolution in emotion and meaning by calculating rates of change of vectors and time dynamics.