Classifying customer experience sentiment using machine learning

A system, comprising:

• a memory;
• and
• at least one processor coupled to the memory and configured to:
• receive data including call transcript text and metadata related to a call;
• preprocess the data to generate preprocessed data;
• apply the preprocessed data to a sentiment classification model trained to produce a plurality of probability values respectively corresponding to a plurality of sentiment categories corresponding to different potential qualities of the call transcript text;
• and
• determine a numeric sentiment classification by comparing the plurality of probability values to a plurality of thresholds respectively corresponding to the plurality of sentiment categories, wherein the numeric sentiment classification corresponds to a quality of the call.

A non-transitory computer-readable device having instructions stored thereon that, when executed by at least one computing device, cause the at least one computing device to perform operations comprising:

• receiving data including call transcript text and metadata related to a call;
• preprocessing the data to generate preprocessed data;
• applying the preprocessed data to a sentiment classification model trained to produce a plurality of probability values respectively corresponding to a plurality of sentiment categories corresponding to different potential qualities of the call transcript text;
• and
• determining a numeric sentiment classification by comparing the plurality of probability values to a plurality of thresholds respectively corresponding to the plurality of sentiment categories, wherein the numeric sentiment classification corresponds to a quality of the call.