A Classification Framework on Opinion Mining for Effective Recommendation Systems

A Classification Framework on Opinion Mining for Effective Recommendation Systems

Mahima Goyal (Ambedkar Institute of Advanced Communication Technologies and Research, India) and Vishal Bhatnagar (Ambedkar Institute of Advanced Communication Technologies and Research, India)
Copyright: © 2017 |Pages: 15
DOI: 10.4018/978-1-5225-0489-4.ch010
OnDemand PDF Download:
$30.00
List Price: $37.50

Abstract

With the recent trend of expressing opinions on the social media platforms like Twitter, Blogs, Reviews etc., a large amount of data is available for the analysis in the form of opinion mining. This analysis plays pivotal role in providing recommendation for ecommerce products, services and social networks, forecasting market movements and competition among businesses, etc. The authors present a literature review about the different techniques and applications of this field. The primary techniques can be classified into Data Mining methods, Natural Language Processing (NLP) and Machine learning algorithms. A classification framework is designed to depict the three levels of opinion mining –document level, Sentence Level and Aspect Level along with the methods involved in it. A system can be recommended on the basis of content based and collaborative filtering
Chapter Preview
Top

Literature Review

A lot of authors have written about opinion mining in different papers. Some of the authors have used machine learning algorithms while some have incorporated Natural Language Processing (NLP) techniques. Turney (2002) performed the classification at document level using unsupervised machine learning technique. Liu (2012) implemented the classification of reviews at document level.

Complete Chapter List

Search this Book:
Reset