Exploring Optimality and Consistency of Supervised Machine Learning Algorithms in Sentiment Analysis

Over the past decade, deep learning has gained massive popularity in the field of machine learning (ML). However, it is more computationally expensive to train a deep learning model that requires the presence of a huge amount of labeled dataset that is limited in availability. Since a smaller dataset is needed to train a model with supervised ML algorithms, making it more feasible in practice, this study aims to investigate the performance of six widely used supervised ML algorithms which include Decision Trees, K-Nearest Neighbors, Logistic Regression, Naive Bayes, Random Forests, and Support Vector Machines in terms of optimality and consistency in the context of sentiment analysis. The findings of this study underscore the importance of adapting a high performing, consistent and predictable supervised ML algorithms to overcome language barriers for reliable sentiment analysis and show that both Logistic Regression and Support Vector Machines are deemed to fulfil these criteria.