An Inventive Movie Suggestion System Using Machine Learning Techniques

With the growth of the internet and its users over the past decade, there is an extremely large magnitude of data online. There are various websites and forums which provide options to review a particular product or place. Hence, sentiment analysis of this opinion data is in demand today. The opinions and sentiments gathered through analysis help in a more accurate way of understanding the customer’s preferences and taste. In this study, our objective is to determine the underlying opinion in the movie reviews which could be positive, neutral, or negative, after pre-processing the data and extracting the important features. Various machine learning algorithms like Support Vector Machine (SVM), Decision trees, Random Forest and Naive Bayes classifier have been used for this classification and the results from all these algorithms are compared in our study. Further, we also appraise the role of pre-processing the online reviews before polarizing them. The outcome of this study shows the different accuracies given by the various classifiers applied on the same movie review dataset and shows that Naive Bayes and Random Forest classifiers perform better than the others.