Abstract

Sentiment analysis is a vital aspect of word processing that is becoming increasingly successful with the advent of machine learning. This article provides a comparative evaluation of machine learning-based analytics theory. Through extensive data analysis, it examines a variety of machine learning algorithms, spanning from Support Vector Machines (SVM) to Naive Bayes, Random Forests, and advanced deep learning models like Recurrent Neural Networks (RNN) and Convolutional Neural Networks (CNNs). Based on performance, scalability, definition and performance of calculations. Additionally, the study investigates the impact of feature representation techniques, including Bag-of-Words, Word Embeddings (e.g., Word2Vec, GloVe), and Transformers (e.g., BERT), on sentiment analysis tasks. Furthermore, the paper discusses the challenges and limitations associated with each approach and identifies potential areas for future research and improvement. The results of this comparative analysis can guide researchers and practitioners in selecting appropriate machine learning models and techniques to perform cognitive analysis in a variety of domains, from social analysis to customer feedback.