SHAP values for Explaining CNN-based Text Classification Models

Abstract

Deep neural networks are increasingly used in natural language processing (NLP) models. However, the need to interpret and explain the results from complex algorithms are limiting their widespread adoption in regulated industries such as banking. There has been recent work on interpretability of machine learning algorithms with structured data. But there are only limited techniques for NLP applications where the problem is more challenging due to the size of the vocabulary, high-dimensional nature, and the need to consider textual coherence and language structure. This paper develops a methodology to compute SHAP values for local explainability of CNN-based text classification models. The approach is also extended to compute global scores to assess the importance of features. The results are illustrated on sentiment analysis of Amazon Electronic Review data.

17 pages, 5 figures

References in corpus (2)

• Effective Use of Word Order for Text Categorization with Convolutional Neural Networks
• Understanding Convolutional Neural Networks for Text Classification

Cited by in corpus (4)

• Sentiment Analysis in Finance: From Transformers Back to eXplainable Lexicons (XLex)
• Realised Volatility Forecasting: Machine Learning via Financial Word Embedding
• Model Explainability in Deep Learning Based Natural Language Processing
• Self-interpretable Convolutional Neural Networks for Text Classification