Exploring Movie Sentiments: A Comparative Analysis of Deep Learning, Machine Learning, and Bayesian Approaches on IMDb Reviews

Sentiment analysis serves a significant function in those large volumes of text data, like product reviews or company feedback, make organizations better informed. Deep learning models, such as Long Short-Term Memory Networks (LSTM), are very proficient in modeling sequential information and, therefore, very effective with regard to sentiment classification tasks like IMDb reviews. Traditional machine learning methods like Multinomial Naive Bayes (MNB) and Logistic Regression (LR) work almost as well. MNB combined with feature extraction like TF-IDF and feature selection techniques such as Information Gain yields nearly 90.59score. LR is simple and reliable and works well on huge text datasets. Using a Voting Assembler, combining predictions from multiple models, leads to improving precision in classification, since each model exploits its strengths and more models increase the chance of getting the right decision when the three mentioned methods are used together in a voting system, this usually surpasses their standalone usage with significant boosts in both performance and robustness in sentiment classification. Other models like In addition, Support Vector Machine, Random Forest, Gradient Boosting, AdaBoost, Decision Tree, KNearest Neighbors, Bagging Classifier, XGBoost, LightGBM, and even Dummy Classifier seem to plug some gaps toward giving a complete classification framework balancing between deep learning methods and classical machine learning techniques. The ensemble approach does improve the accuracy and interpretability of sentiment analysis. We also added Bernoulli Naive Bayes (BNB), GRU (Gated Recurrent Units), RNN (Recurrent Neural Networks), and Gaussian Naive Bayes (GNB) in an attempt to further diversify the model set and boost the performance of sentiment classification. This has greatly diversified the kinds of data one uses to guarantee the reliability and effectiveness of the whole process undertaken in the sentiment analysis.