Advancing Sentiment Analysis of IMDB Movie Reviews with a Hybrid Multinomial Naive Bayes and LSTM Approach

Natural Language Processing (NLP) is increasingly pivotal in the natural sciences, with sentiment analysis emerging as a crucial application in the era of big data. Efficiently and accurately extracting meaningful insights from extensive textual data remains a significant challenge. This study explores sentiment analysis within the context of movie reviews, a domain where understanding nuanced viewer perceptions can influence industry outcomes. Traditional methods often struggle with the complexity of language, where identical phrases may convey different sentiments based on their context. To address these challenges, this research implements a hybrid approach, combining a Multinomial Naive Bayes Classifier and a Long Short-Term Memory (LSTM) model developed in TensorFlow. Our integrated methodology not only advances the robustness of sentiment analysis tools but also achieves a notable accuracy of 96.25%, underscoring its potential to enhance complex NLP tasks in real-world applications.