Machine learning from tone analysis in online customer service

A computer program product for tone analysis for online customer service, the computer program product comprising a non-statutory computer readable storage medium having program instructions embodied therewith, the program instructions executable by a processor to cause the processor to:

• receive, by the processor, adjusted labeled data;
• execute, by the processor, a computing factor analysis for the received adjusted labeled data to obtain and store emotional tone factors in a memory device;
• execute, by the processor, a tone modified latent Dirichlet allocation (T-LDA) machine learning model that models tone intensity, wherein the T-LDA machine learning model uses the emotional tone factors and integrates the adjusted labeled data for processing words;
• identify, by the processor, representative words for each emotional tone factor by the T-LDA machine learning model, wherein the representative words are identified by the T-LDA machine learning model based on computing posterior probabilities and adjusting the probabilities based on an auxiliary topic, and the T-LDA machine learning model outputs examples of tone;
• and
• execute, by the processor, model prediction processing using the examples of tone and unlabeled data to output predicted tone for tone labeling for the unlabeled data.

An apparatus comprising:

• a memory configured to store instructions;
• and
• a processor configured to execute the instructions to:
• receive adjusted labeled data;
• execute a computing factor analysis for the received adjusted labeled data to obtain and store emotional tone factors in a memory device;
• execute a tone latent Dirichlet allocation (T-LDA) machine learning model that models tone intensity, wherein the T-LDA machine learning model uses the emotional tone factors and integrates the adjusted labeled data for processing words;
• identify representative words for each emotional tone factor by the T-LDA machine learning model, wherein the representative words are identified by the T-LDA machine learning model based on computing posterior probabilities and adjusting the probabilities based on an auxiliary topic, and the T-LDA machine learning model outputs examples of tone;
• and
• execute model prediction processing using the examples of tone and unlabeled data to output predicted tone for tone labeling for the unlabeled data.