Self-explaining Neural Network for Multi-criteria Sentiment Analysis

Xuan-Truong Hoang, Vu-Linh Nguyen, Takayuki Kitagawa, Van-Nam Huynh

Abstract: Despite a rich literature on explainable classification, to our knowledge, there is a lack of classification methods that come with reliable user-orientated explanations supporting the predictions. To complement the existing literature on explainable classification, we propose a Self-Explaining Neural Network for Multi-Criteria Sentiment Analysis (SENN4MCSA) which consists of three key components: topic modeling, which extracts relevant topics from the training data, topics-criteria alignment, which partitions the relevant topics into the evaluation criteria given by the end-user, and self-explanation sentiment analysis, which consists of training a self-explanation classification and explanation step based on domain knowledge extracted from the topics-criteria alignment phase. More precisely, the output of the topics-criteria alignment is taken into account in the explanation step to provide user-orientated explanations supporting the prediction of the self-explanation classification.

External IDs:dblp:conf/iukm/HoangNKH25