<![CDATA[This paper presents an analysis of the Internet Movie Database (IMDb) using the Support Vector Machine (SVM) algorithm for sentiment classification. IMDb, as one of the largest online movie review platforms, offers a vast dataset of user reviews, which can be leveraged to analyze public opinion on movies. The goal of this study is to classify movie reviews as positive or negative using SVM, a machine learning algorithm known for its effectiveness in binary classification tasks. The dataset, consisting of thousands of IMDb reviews, undergoes pre-processing steps such as tokenization, removal of stop words, and text vectorization using Term Frequency-Inverse Document Frequency (TF-IDF). The SVM algorithm is then applied to this processed data to train the model, which is evaluated based on its accuracy, precision, recall, and F1-score. Experimental results indicate that the SVM model performs with high accuracy, proving its reliability in sentiment analysis tasks for large-scale movie review datasets. This paper also discusses the advantages of using SVM over other machine learning algorithms and highlights areas for future improvement, including incorporating more nuanced sentiment categories and optimizing the model's hyperparameters.]]>
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