This project uses machine learning models, such as Random Forest and Support Vector Machines (SVM), for sentiment analysis on Twitter data. The system collects tweets via Twitter API and processes them using NLP techniques like tokenization and lemmatization. Machine learning models are then trained to predict sentiment categories. This approach helps improve the accuracy of sentiment classification and can be applied to large datasets for comprehensive sentiment analysis.
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