Using Structured Representation and Data: A Hybrid Model for Negation and Sentiment in Customer Service Conversations
Table 8: Sentiment classification evaluation, using different classifiers on the test set.
['Classifier', 'Positive Sentiment Precision', 'Positive Sentiment Recall', 'Positive Sentiment Fscore']
show that the antonym based learned representations are more useful for sentiment task as compared to prefixing with NOT_. The proposed CNN-LSTMOur-neg-Ant improves upon the simple CNNLSTM-w/o neg. baseline with F1 scores improving from 0.72 to 0.78 for positive sentiment and from 0.83 to 0.87 for negative sentiment.