Machine Learning Approaches for Natural Language Processing and Sentiment Analysis

Abstract

The exponential growth of digital content has necessitated the development of effective techniques in natural language processing (NLP) and sentiment analysis. This review paper aims to provide a comprehensive overview of machine learning approaches employed in NLP tasks, with a specific focus on sentiment analysis. We explore various algorithms such as support vector machines (SVM), recurrent neural networks (RNN), and transformer models that have shown promising results in analyzing and classifying sentiments expressed in textual data. Additionally, we explore pre-processing techniques like tokenization and feature engineering that play a vital role in enhancing the performance of these machine learning models. Through an extensive evaluation using benchmark datasets, we compare the strengths, weaknesses, and suitability of different machine learning methods for sentiment analysis tasks. Furthermore, we highlight recent advancements such as transfer learning and explainable AI that have demonstrated potential in improving NLP capabilities. Finally, we discuss emerging trends and future research directions aimed at leveraging machine learning advancements to further enhance natural language processing techniques.