Machine Learning Approach for Sentiment Analysis of Online Reviews

Abstract

Sentiment analysis has emerged as a powerful tool for extracting insights from the vast volume of online reviews generated by users across various platforms. This study focuses on employing machine learning techniques for sentiment analysis to classify online reviews into positive, negative, or neutral categories. Traditional sentiment analysis approaches, such as rule-based or lexicon-based methods, often fail to capture the complexity of natural language, including context, sarcasm, and ambiguous expressions. In contrast, machine learning models, particularly supervised learning algorithms like Support Vector Machines (SVM) and Naive Bayes, as well as deep learning models such as Long Short-Term Memory (LSTM) networks and Convolutional Neural Networks (CNNs), provide more accurate and scalable solutions. This research explores the application of these machine learning techniques, comparing their performance on various datasets of online reviews. The study aims to identify the most effective methods for handling diverse linguistic patterns and contexts, providing businesses with valuable insights into customer sentiment. The findings demonstrate the potential of machine learning to enhance the accuracy and efficiency of sentiment analysis, driving informed decision-making and improved customer experiences.