Comparative Research in Sentiment Analysis Using Machine Learning Technique

In today’s digital background, sentiment analysis has become an essential factor of Natural Language Processing (NLP), offering valuable insights from vast online data sources. This paper presents a comparative analysis of sentiment analysis techniques leveraging machine learning. As digital content continues to expand rapidly, decoding public sentiment has become increasingly important for businesses and researchers. The study examines various approaches, including traditional machine learning methods like Support Vector Machines (SVM) and Naïve Bayes (NB), as well as deep learning models such as Convolutional Neural Networks (CNN) and Long Short-Term Memory (LSTM) networks. It also explores hybrid frameworks that combine the strength of both paradigms. By evaluating the advantages and limitations of these models, especially within the context of e-commerce, this review provides a comprehensive understanding of their performance. Additionally, the paper addresses critical challenges such as real-time sentiment detection and multi-label classification. Through a synthesis of existing research, it highlights promising directions for future work and contributes to the development of more accurate and practical sentiment analysis solutions across various applications.