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2018 | OriginalPaper | Chapter

A Nonnegative Matrix Factorization Based Approach to Extract Aspects from Product Reviews

Authors : Debaditya Barman, Nirmalya Chowdhury

Published in: Advanced Computational and Communication Paradigms

Publisher: Springer Singapore

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Abstract

Due to the unstructured nature of review text, it is very hard to develop an automated opinion mining application to compare various product models based on their various aspects to make a purchase decision. Over the year, various data mining techniques have been proposed to extract aspects of the products. In this paper, we have proposed a technique based on the nonnegative matrix factorization to extract aspects of a product category. Performance of our proposed method has been compared with a very popular aspect extraction technique based on probabilistic latent semantic analysis. We have also given a comparison between common aspects of a particular model under a specific product category from various manufacturers. These comparisons are based on the sentiments expressed by the users on these aspects. These sentiments expressed in various aspects have been extracted using an unsupervised technique.

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Metadata
Title
A Nonnegative Matrix Factorization Based Approach to Extract Aspects from Product Reviews
Authors
Debaditya Barman
Nirmalya Chowdhury
Copyright Year
2018
Publisher
Springer Singapore
Book
Advanced Computational and Communication Paradigms

Print ISBN: 978-981-10-8236-8

Electronic ISBN: 978-981-10-8237-5

Copyright Year: 2018

DOI
https://doi.org/10.1007/978-981-10-8237-5_25