Text classification is a task of distribution of collection of predefined classes to free-text. Text classifiers are not able to organize, structure, and reason just about something. In this work we have used random forest and naïve Bayes algorithms to perform document classification task. We have trained the machine learning models to inference the respective class of the documents. By working on very big data sets of movie reviews the chosen machine learning models predict whether the reviews are positive or negative and then we analyse and compare the results of each model’s individual confusion matrix like precision, recall, f1-score & support. An important observation is that for the same input data random forest provides more relevant results as compared to naïve bayes algorithm. But as the training data grows naïve bayes also performs equally good as random forest.

Text Classification, Naïve Bayes, Random Forest, Machine Learning

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