An explainable machine learning model for sentiment analysis of online reviews

Over the last two decades and with the widespread use of social media and e-commerce sites, scientific research in the field of sentiment analysis (SA) has made considerable progress in terms of obtained results and the number of published articles. The greatest part of this progress has been achieved by SA systems based on machine learning. However, most of these systems lack transparency and explainability, making it difficult to understand their internal processes and consequently to trust their decisions and predictions. To address this problem, we propose an easy-to-use machine learning model based on an intuitive geometric approach for SA of online reviews. For linearly separable data, we adopt an iterative algorithm called the explainable algorithm for binary linear classification (EABLC) to determine the maximum-margin separating hyperplane based on the geometric concept of the convex hull. As an extension of EABLC, two new algorithms are further proposed, namely, the soft explainable algorithm for binary classification and the explainable algorithm for binary polyhedral classification, to avoid outliers and deal with linearly nonseparable data. Aside from its simplicity and intuitiveness, experimental results on the Amazon product and movie review sentiment datasets demonstrate the efficiency and robustness of our model, which outperforms ten benchmark classification algorithms.