A Comparative Analysis of Local Explainability of Models for Sentiment Detection

Abstract

Sentiment analysis is one of the crucial tasks in Natural Language Processing (NLP) which refers to classifying natural language sentences by their positive or negative sentiments. In many existing deep learning-based models, providing an explanation of a sentiment might be as necessary as the prediction itself. In this study, we use four different classification models applied to the sentiment analysis of the Internet Movie Database (IMDB) reviews, and investigate the explainability of results using Local Interpretable Model-agnostic Explanation (LIME). Our results reveal how the attention-based models, such as Bidirectional LSTM (BiLSTM) and fine-tuned Bidirectional Encoder Representations from Transformers (BERT) would focus on the most relevant keywords.