Interpretability of Computational Models for Sentiment Analysis

Abstract

Sentiment analysis, which is also known as opinion mining, has been an increasingly popular research area focusing on sentiment classification/regression. In many studies, computational models have been considered as effective and efficient tools for sentiment analysis . Computational models could be built by using expert knowledge or learning from data. From this viewpoint, the design of computational models could be categorized into expert based design and data based design. Due to the vast and rapid increase in data, the latter approach of design has become increasingly more popular for building computational models. A data based design typically follows machine learning approaches, each of which involves a particular strategy of learning. Therefore, the resulting computational models are usually represented in different forms. For example, neural network learning results in models in the form of multi-layer perceptron network whereas decision tree learning results in a rule set in the form of decision tree. On the basis of above description, interpretability has become a main problem that arises with computational models. This chapter explores the significance of interpretability for computational models as well as analyzes the factors that impact on interpretability. This chapter also introduces several ways to evaluate and improve the interpretability for computational models which are used as sentiment analysis systems. In particular, rule based systems , a special type of computational models, are used as an example for illustration with respects to evaluation and improvements through the use of computational intelligence methodologies.