Authors: R.S. Deshpande, Kirti Gautam Latake, Santosh Gaikwad, Arshiya Khan,
Abstract: Sentiment analysis has emerged as a vital technique in natural language processing, enabling machines to identify and interpret emotional tone in text. This paper presents a comprehensive review of traditional machine learning and recent deep learning approaches for sentiment classification. It highlights the evolution from classical algorithms to advanced models like LSTM and ALBERT, emphasizing their accuracy and applicability. An experimental evaluation using the Amazon Fine Food Reviews dataset confirms the superiority of deep learning models. The paper also discusses key challenges and outlines future directions to enhance the interpretability, scalability, and ethical use of sentiment analysis across various domains.
DOI: http://doi.org/
