Abstract
Sentiment analysis, a critical tool in natural language processing (NLP), has seen significant advancements with the rise of machine learning and deep learning techniques. This paper explores the evolution of sentiment analysis from traditional models, such as CNN and RNN, to more sophisticated approaches like LSTM, LSTM-CNN, and BERT. Using the IMDb dataset for binary classification of sentiment, we utilize metrics like F-Score, Precision, Accuracy, and Recall to compare the way these models perform. Despite the progress made, sentiment analysis still faces challenges, particularly in handling multiple languages, incorporating multimedia content, and addressing privacy and ethical concerns. This paper also discusses future directions for sentiment analysis, including the development of models for low-resource languages, real-time sentiment analysis, and the ethical implications of data handling. Overall, the study highlights the potential of sentiment analysis and the opportunities for improvement through continued research and innovation.
Published Version
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