  Sentiment analysis has been widely used by businesses for social media
opinion mining, especially in the financial services industry, where customers'
feedbacks are critical for companies. Recent progress of neural network models
has achieved remarkable performance on sentiment classification, while the lack
of classification interpretation may raise the trustworthy and many other
issues in practice. In this work, we study the problem of improving the
explainability of existing sentiment classifiers. We propose two data
augmentation methods that create additional training examples to help improve
model explainability: one method with a predefined sentiment word list as
external knowledge and the other with adversarial examples. We test the
proposed methods on both CNN and RNN classifiers with three benchmark sentiment
datasets. The model explainability is assessed by both human evaluators and a
simple automatic evaluation measurement. Experiments show the proposed data
augmentation methods significantly improve the explainability of both neural
classifiers.
