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

Abstract

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 Naïve Bayes' and compares their overall accuracy, precisions as well as recall values. 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.

A Study of Product Trend Analysis of Review Datasets using Naive Bayes’, K-NN and SVM Classifiers

“…The overall accuracies of three algorithms in eight rounds of experiments are given below in Table 2. Diagrammatic representation of accuracy in the test is shown Fig 3. dataset below [12]. In the below tables, we are included the SVM classifier evaluation for opinion mining of movie reviews (Table 3).…”

“…We have also shown how SVM gives better results on movie reviews dataset [12] compare to K-NN and Naïve Bayes algorithms. Thus we can conclude that SVM classifier can be used successfully for analyzing the customer reviews for products.…”

“…The main comparison with the paper [12] lies that this work uses the most popular support vector machine (SVM) classification algorithm for analysis and prediction purpose. In paper [12], only K-NN and Native Bayes supervised machine learning algorithms are used to analyze the movie reviews and hotel reviews.…”

“…Then the overall positive or negative score for the text is evaluated and perform comparison between the positive and negative documents based on the high density of the positive or negative words present in the documents. In paper [6], a work on sentiment classification has done using single pass clustering for K-Nearest Neighbor algorithm.A very similar work on hotel customer reviews and movie reviews are analyzed using the well-known K-NN and Naïve Bayes classification algorithms in paper [12].…”

“…There are various machine learning algorithms which are used for classification, analysis and prediction of useful data from a large dataset. This paper mainly focuses on three widely used supervised machine learning algorithms (Naïve-Bayes, K-Nearest Neighbour and Support vector machine) for product trend analysis [12]. Here, we are using three factors accuracies, precisions, recall values with the applications of above classification algorithms for analysis the The challenges in sentiment analysis are the words which can be used in dual senses.…”