Multi Criteria Decision Making Approach For Product Aspect Extraction And Ranking In Aspect-Based Sentiment Analysis

Identifying product aspects in customer reviews can have a great influence on both business strategies as well as on customers’ decisions. Presently, most research focuses on machine learning, statistical, and Natural Language Processing (NLP) techniques to identify the product aspects in customer r...

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Bibliographic Details
Main Author:	Alrababah, Saif Addeen Ahmad Ali
Format:	Thesis
Language:	English
Published:	2018
Subjects:	QA75.5-76.95 Electronic computers Computer science
Online Access:	http://eprints.usm.my/43741/1/SAIF%20ADDEEN%20AHMAD%20ALI%20ALRABABAH.pdf
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id	my-usm-ep.43741
record_format	uketd_dc
spelling	my-usm-ep.437412019-04-12T05:24:51Z Multi Criteria Decision Making Approach For Product Aspect Extraction And Ranking In Aspect-Based Sentiment Analysis 2018-04 Alrababah, Saif Addeen Ahmad Ali QA75.5-76.95 Electronic computers. Computer science Identifying product aspects in customer reviews can have a great influence on both business strategies as well as on customers’ decisions. Presently, most research focuses on machine learning, statistical, and Natural Language Processing (NLP) techniques to identify the product aspects in customer reviews. The challenge of this research is to formulate aspect identification as a decision-making problem. To this end, we propose a product aspect identification approach by combining multi-criteria decision-making (MCDM) with sentiment analysis. The suggested approach consists of two stages namely product aspect extraction and product aspect ranking. 2018-04 Thesis http://eprints.usm.my/43741/ http://eprints.usm.my/43741/1/SAIF%20ADDEEN%20AHMAD%20ALI%20ALRABABAH.pdf application/pdf en public phd doctoral Universiti Sains Malaysia Pusat Pengajian Sains Komputer
institution	Universiti Sains Malaysia
collection	USM Institutional Repository
language	English
topic	QA75.5-76.95 Electronic computers Computer science
spellingShingle	QA75.5-76.95 Electronic computers Computer science Alrababah, Saif Addeen Ahmad Ali Multi Criteria Decision Making Approach For Product Aspect Extraction And Ranking In Aspect-Based Sentiment Analysis
description	Identifying product aspects in customer reviews can have a great influence on both business strategies as well as on customers’ decisions. Presently, most research focuses on machine learning, statistical, and Natural Language Processing (NLP) techniques to identify the product aspects in customer reviews. The challenge of this research is to formulate aspect identification as a decision-making problem. To this end, we propose a product aspect identification approach by combining multi-criteria decision-making (MCDM) with sentiment analysis. The suggested approach consists of two stages namely product aspect extraction and product aspect ranking.
format	Thesis
qualification_name	Doctor of Philosophy (PhD.)
qualification_level	Doctorate
author	Alrababah, Saif Addeen Ahmad Ali
author_facet	Alrababah, Saif Addeen Ahmad Ali
author_sort	Alrababah, Saif Addeen Ahmad Ali
title	Multi Criteria Decision Making Approach For Product Aspect Extraction And Ranking In Aspect-Based Sentiment Analysis
title_short	Multi Criteria Decision Making Approach For Product Aspect Extraction And Ranking In Aspect-Based Sentiment Analysis
title_full	Multi Criteria Decision Making Approach For Product Aspect Extraction And Ranking In Aspect-Based Sentiment Analysis
title_fullStr	Multi Criteria Decision Making Approach For Product Aspect Extraction And Ranking In Aspect-Based Sentiment Analysis
title_full_unstemmed	Multi Criteria Decision Making Approach For Product Aspect Extraction And Ranking In Aspect-Based Sentiment Analysis
title_sort	multi criteria decision making approach for product aspect extraction and ranking in aspect-based sentiment analysis
granting_institution	Universiti Sains Malaysia
granting_department	Pusat Pengajian Sains Komputer
publishDate	2018
url	http://eprints.usm.my/43741/1/SAIF%20ADDEEN%20AHMAD%20ALI%20ALRABABAH.pdf
_version_	1747821269585231872

Multi Criteria Decision Making Approach For Product Aspect Extraction And Ranking In Aspect-Based Sentiment Analysis

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