Machine Learning vs. Deep Learning for Personalized Medicine Recommendations: A Sentiment Analysis Perspective

The paper provides a comprehensive comparative analysis of sentiment analysis algorithms, which includes Support Vector Machines (SVM), Convolutional Neural Networks (CNN), K-Nearest Neighbors (KNN), Logistic Regression, Simple Neural Network, and Naive Bayes Utilizing a variety of datasets from social media sentiments and product reviews, the study thoroughly evaluates both the computational complexities and practical performance of these methods. Through comprehensive experiments and analysis of metrics such as accuracy, precision, recall, and F1-score, the study reveals the distinct effectiveness of each algorithm for different sentiment analysis tasks. The findings highlights the strengths and limitations of eachapproach, offering valuable insights for practitioners seeking to navigate the landscape of sentiment analysis techniques. By contributing towards the ongoing discourse on advancing sentiment analysis in the context of big data and computational linguistics, this study aims to inform algorithm selection and promote advancements in NLP research.