Sentiment Analysis in the Age of AI: A Comparative Study of Machine Learning and Deep Learning Methods

Sentiment analysis is a technique used to detect and categorize the emotional tone or opinion of text provided to it. This research aims to compare various methodologies in sentiment analysis, specifically traditional Machine Learning techniques such as Naïve Bayes and Support Vector Machine, alongside advanced Deep Learning approaches like Convolutional Neural Networks and Recurrent Neural Networks. Additionally, Hybrid Models combine Machine Learning and Deep Learning techniques to enhance sentiment classification. According to extensive research, it has been found that Machine Learning models are useful for smaller datasets having low computational requirements, even though traditional approaches for processing complex linguistic patterns are far more efficient than Deep Learning. Deep Learning models perform better when dealing with complex, large-scale data. This paper discusses each method's strengths and limitations while insights into their practical application are provided. Findings suggest that the decision to utilize Machine Learning or Deep Learning should be influenced by considerations including the size of the dataset, available computational resources, and the necessity for real-time processing.