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Sentiment analysis (SA) is a branch of opinion mining that focuses on obtaining people’s thoughts and feelings about a specific subject from systematic, semi-structured, or unorganized text data. In this paper, the efficacy of four ML classifiers i.e. Decision Tree (DT), Random Forest (RF), Logistic Regression (LR) and Multinomial NB is analyzed on IMDB dataset. The main objective of the proposed work is to analyze which classifier shows best results on the given dataset. To achieve this objective, necessary movie review or comment data is taken from IMDB dataset that is available on Kaggle. However, this dataset is not balanced and contains a lot of unnecessary and redundant data that needs to be eliminated, therefore, pre-processing is must. During the pre-processing phase, tokenization, stemming, stop words removal and segregation like techniques are implemented to make the data balanced and normalized. After this, the given dataset is divided into subsets by using k-fold cross validation approach. The main motive for doing so is to train the ML classifiers effectively on various combinations of data so that its accuracy can be enhanced. Finally, the classification is performed by DT, RF, LR and Multinomial NB classifiers as per the training provided to them. The efficacy of the system is analyzed using MATLAB on IMDB dataset for every fold. Simulation results revealed that LR classifiers is outperforming DT, RF and Multinomial NB in terms of accuracy, precision, recall and F1-Score as well, to prove its supremacy.
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