An empirical study of sentiment analysis utilizing machine learning and deep learning algorithms

Abstract

Among text-mining studies, one of the most studied topics is the text classifcation task applied in various domains, including medicine, social media, and academia. As a sub-problem in text classifcation, sentiment analysis has been widely investigated to classify often opinion-based textual elements. Specifcally, user reviews and experiential feedback for products or services have been employed as fundamental data sources for sentiment analysis eforts. As a result of rapidly emerging technological advancements, social media platforms such as Twitter, Facebook, and Reddit, have become central opinion-sharing mediums since the early 2000s. In this sense, we build various machine-learning models to solve the sentiment analysis problem on the Reddit comments dataset in this work. The experimental models we constructed achieve F1 scores within intervals of 73–76%. Consequently, we present comparative performance scores obtained by traditional machine learning and deep learning models and discuss the results.