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

TL;DR

This paper compares Naive Bayes and K-NN classifiers for sentiment analysis of movie and hotel reviews, demonstrating Naive Bayes's superior performance on movie reviews and similar accuracy on hotel reviews.

Contribution

It introduces a sentiment-focused web crawling framework and evaluates two machine learning algorithms for sentiment classification on review datasets.

Findings

Naive Bayes outperforms K-NN on movie reviews.

Both algorithms have similar accuracy on hotel reviews.

Statistical methods effectively capture sentiment polarity.

Abstract

The advent of Web 2.0 has led to an increase in the amount of sentimental content available in the Web. Such content is often found in social media web sites in the form of movie or product reviews, user comments, testimonials, messages in discussion forums etc. Timely discovery of the sentimental or opinionated web content has a number of advantages, the most important of all being monetization. Understanding of the sentiments of human masses towards different entities and products enables better services for contextual advertisements, recommendation systems and analysis of market trends. The focus of our project is sentiment focussed web crawling framework to facilitate the quick discovery of sentimental contents of movie reviews and hotel reviews and analysis of the same. We use statistical methods to capture elements of subjective style and the sentence polarity. 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. It was seen that in case of movie reviews Naive Bayes gave far better results than K-NN but for hotel reviews these algorithms gave lesser, almost same accuracies.