Quantifying customer care utilizing emotional assessments

A computer program product for reducing cost and improving real-time performance for analyzing and representing customer care communication progression based on tone assessments, the computer program product comprising a computer readable storage medium having program instructions embodied therewith, the program instructions executable on a processing circuit to cause the processing circuit to perform the method comprising:

• monitoring communications between a plurality of customers and a plurality of representatives to form a plurality of interaction histories;
• analyzing a text form of the plurality of interaction histories according to a Tone Latent Dirichlet Allocation [T-LDA] model to determine a plurality of conversation tones;
• mapping the plurality of conversation tones to at least two conversation phases;
• providing a representation of the conversation tones identifying a first customer tone at a first conversation phase and a second customer tone at a second conversation phase;
• receiving by the T-LDA model a first utterance from the first customer;
• categorizing the first utterance into a semantic category wherein the semantic category is one of a tone-related category, a domain-specific category, and an auxiliary category;
• identifying a topic word in the first utterance associated with the semantic category;
• associating the topic word with a topic word label;
• and
• identifying a representative utterance associated with the topic word label.

An information handling system for reducing cost and improving real-time performance for analyzing and representing customer care communication progression based on tone assessments, the information handling system comprising:

• at least one processor;
• a memory coupled to the at least one processor;
• a set of instructions stored in the memory and executed by the at least one processor wherein the set of instructions perform operations including:
• monitoring communications between a plurality of customers and a plurality of representatives to form a plurality of interaction histories;
• analyzing a text form of the plurality of interaction histories according to a Tone Latent Dirichlet Allocation [T-LDA] model to determine a plurality of conversation tones;
• mapping the plurality of conversation tones to at least two conversation phases;
• providing a representation of the conversation tones identifying a first customer tone at a first conversation phase and a second customer tone at a second conversation phase;
• receiving by the T-LDA model a first utterance from the first customer;
• categorizing the first utterance into a semantic category wherein the semantic category is one of a tone-related category, a domain-specific category, and an auxiliary category;
• identifying a topic word in the first utterance associated with the semantic category;
• associating the topic word with a topic word label;
• and
• identifying a representative utterance associated with the topic word label.