Paper Title
DEEP LEARNING MODELS FOR SENTIMENT ANALYSIS CLASSIFICATION: REVIEW

Abstract
Sentiment analysis is a powerful tool in natural language processing (NLP), providing the understanding of public sentiment, consumer responses, and social behaviors. Sentiment analysis of texts presents a unique challenge, because of the brevity, informality, ambiguity, and lack of context. In the years past, research has shown the advance of machine learning and deep learning in accurately identifying the sentiment of texts. Traditional machine learning models struggle to address these complexities, while transfer learning models have shown remarkable progress by improving the understanding of semantic and contextual information. This paper explores the evolution of sentiment analysis in texts, focusing on the impact of transfer learning models. It reviews different approaches in machine learning, deep learning, and hybrid methods, with its strengths and weaknesses highlighted. It also addresses continuous challenges such as computational complexity, unbalanced datasets, and complex expressions such as sarcasm. Finally, the research identified key directions for future research aimed at developing more efficient and accurate models for analyzing sentiment in text. Keywords - Natural Language Processing; Sentiment analysis; Transfer Learning; Deep Learning