Sentiment Analysis of Review Datasets Using Naïve Bayes‘ and K-NN Classifier

The paper elaborately discusses two supervised machine learning algorithms: K-Nearest Neighbour(K-NN) and Naive Bayes and compares their overall accuracy, precisions as well as recall values and it was seen that in case of movie reviews Naïve Bayes gave far better results than K-NN but for hotel reviews these algorithms gave lesser, almost same accuracies.

Abstract

The advent of Web 2.0 has led to an increase in the amount of sentimental\ncontent available in the Web. Such content is often found in social media web\nsites in the form of movie or product reviews, user comments, testimonials,\nmessages in discussion forums etc. Timely discovery of the sentimental or\nopinionated web content has a number of advantages, the most important of all\nbeing monetization. Understanding of the sentiments of human masses towards\ndifferent entities and products enables better services for contextual\nadvertisements, recommendation systems and analysis of market trends. The focus\nof our project is sentiment focussed web crawling framework to facilitate the\nquick discovery of sentimental contents of movie reviews and hotel reviews and\nanalysis of the same. We use statistical methods to capture elements of\nsubjective style and the sentence polarity. The paper elaborately discusses two\nsupervised machine learning algorithms: K-Nearest Neighbour(K-NN) and Naive\nBayes and compares their overall accuracy, precisions as well as recall values.\nIt was seen that in case of movie reviews Naive Bayes gave far better results\nthan K-NN but for hotel reviews these algorithms gave lesser, almost same\naccuracies.\n