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-transitory computer readable storage medium having program instructions embodied therewith, the program instructions executable by a processor to cause the processor to:

• crawl, by the processor, a network for raw data;
• receive, by the processor, refined emotion metrics for the raw data;
• receive, by the processor, labels for the raw data using refined emotion metrics;
• perform, by the processor, factor analysis for labeled data to obtain emotional tone factors;
• receive, by the processor, adjusted labeled data based on the emotional tone factors;
• analyze, by the processor, words using a tone latent Dirichlet allocation (T-LDA) model that models tone intensity using the emotional tone factors and integrating the adjusted labeled data;
• and
• provide, by the processor, representative words for each emotional tone factor based on using the tone model, wherein the representative words are obtained using the T-LDA model based on determining posterior probabilities and adjusting the probabilities based on an auxiliary topic.

An apparatus comprising:

• a memory configured to store instructions;
• and
• a processor configured to execute the instructions to:
• crawl a network for raw data;
• receive refined emotion metrics for the raw data;
• receive labels for the raw data using refined emotion metrics;
• perform factor analysis for labeled data to obtain emotional tone factors;
• receive adjusted labeled data based on the emotional tone factors;
• analyze words using a tone latent Dirichlet allocation (T-LDA) model that models tone intensity using the emotional tone factors and integrating the adjusted labeled data;
• and
• provide representative words for each emotional tone factor based on using the tone model, wherein the representative words are obtained using the T-LDA model based on determining posterior probabilities and adjusting the probabilities based on an auxiliary topic.