Sentiment Analysis: Machine Learning approaches comparison

Abstract

Text serves as the predominant medium for communication across the internet, notably prevalent on social media platforms like Facebook and Instagram, discussion forums such as Reddit and Quora, as well as micro-blogging sites like Twitter and Tumblr. The extensive utilization of text has generated copious amounts of data, consequently giving rise to the emergence of a burgeoning field of study known as Natural Language Processing (NLP). Within the domain of NLP, Sentiment Analysis (SA) has garnered considerable attention. SA is focused on the extraction of user sentiments from textual content. This subfield has witnessed a surge of interest within the scientific community, leading to the publication of numerous research papers showcasing diverse approaches, methodologies, and perspectives. In this paper, we embark on a comparative analysis of some of the most frequently employed machine learning techniques within the realm of Sentiment Analysis. Our evaluation revolves around assessing the performance of each model using metrics such as accuracy and Fl-score, with a specific focus on a dataset comprised of movie reviews.