Sentiment Analysis of Review Datasets Using Naive Bayes and K-NN Classifier
This work addresses sentiment analysis for web content like reviews to improve services such as advertising and recommendations, but it is incremental as it applies existing methods to new datasets.
The paper tackled sentiment analysis of movie and hotel reviews by comparing Naive Bayes and K-NN classifiers, finding that Naive Bayes performed far better for movie reviews but both algorithms gave similar, lower accuracies for hotel reviews.
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.