# Improving the Explainability of Neural Sentiment Classifiers via Data Augmentation

arXiv: https://arxiv.org/abs/1909.04225 · HTML: https://mlsanity.com/paper/1909.04225/

## Key facts

- Cited by 2 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 10 Sep 2019.

## Built on by (most cited first, 2 of 2)

- [Improving Interpretability via Explicit Word Interaction Graph Layer](https://mlsanity.com/paper/2302.02016/) (2023)
- [On the Evaluation of the Plausibility and Faithfulness of Sentiment Analysis Explanations](https://mlsanity.com/paper/2210.06916/) (2022)

## Abstract (arXiv)

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.