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. We implement SENN4MCSA by employing BERTopic, manual topics-criteria alignment based on domain experience, and SELFEXPLAIN as concrete methods for doing topic modeling, topics-criteria alignment, and self-explanation classification respectively. We assess the potential advantages of the proposed SENN4MCSA on a TripAdvisor data set. The user-orientated explanations are assessed by 4 sentiment criteria, which are often used to assess the reviews on this specific data set: room quality, value, location, and service. The empirical evidence suggests that SENN4MCSA can provide promising levels of predictive performance, and reliable user-orientated explanations.