Acoustic and Lexical Sentiment Analysis for Customer Service Calls

We describe the development of a sentiment analysis system for customer service calls, starting with the data acquisition and labeling, and proceeding to the algorithmic information extraction and modeling process from both spoken words and their acoustic expression. The proposed system is based on the combination of multiple acoustic and lexical models in a late fusion approach. Acoustic aspects of sentiment are captured by utterance-level features based on aggregated openSMILE and raw cepstral features, and further augmented with an energy contour model. Lexical aspects are captured by back-off n-gram language models. These models are found to combine effectively, showing different strengths as pertains to positive and negative sentiment detection.