Lopamudra Dey

Work place: Department of Computer Science & Engineering, Heritage Institute of Technology, Kolkata, India

E-mail: lopamudra.dey@heritageit.edu


Research Interests: Image Compression, Image Manipulation, Network Architecture, Image Processing, Data Mining, Data Compression, Data Structures and Algorithms


Lopamudra Dey: She has completed B-Tech from West Bengal University of Technology, India on Computer Science & Engineering in the year 2009. She has received a Bronze medal in her Bachelor degree. She has completed Master of Technology from University of Kalyani, India in the year of 2011. Now, working as an Assistant Professor at Department of Computer Science & Engineering in Heritage Institute of Technology, Kolkata. Her areas of interests are Bioinformatics, Data Mining, Image Processing and Network Security.

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

By Lopamudra Dey Sanjay Chakraborty Anuraag Biswas Beepa Bose Sweta Tiwari

DOI: https://doi.org/10.5815/ijieeb.2016.04.07, Pub. Date: 8 Jul. 2016

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.

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