A Deep Learning System for Sentiment Analysis of Service Calls
Table 1: Binary classification of sentiment polarity on test data: Accuracy (Acc.), weighted F1-score (F1 (w)), precision (Prec.) and recall (Rec.) for the nonnegative (+) and negative (-) classes.
['Methods', 'Text SVM', 'Text LSTM', 'Text BLSTM', 'Text Vader', 'Text AWS', 'Text Google', 'Audio Elastic-Net', 'Audio KNN', 'Audio RF', 'Audio GMM', 'Audio BLSTM']
The semantic knowledge based classifiers have more than 20% higher F1-score than the general approaches. The classifiers are trained on DLT={transcription data, chat data}. The overall F1-score is more than 10% higher than the classifiers trained on call transcription only data.