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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Main Author: Alrababah, Saif Addeen Ahmad Ali
Format: Thesis
Language:English
Published: 2018
Subjects:
Online Access:http://eprints.usm.my/43741/1/SAIF%20ADDEEN%20AHMAD%20ALI%20ALRABABAH.pdf
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author Alrababah, Saif Addeen Ahmad Ali
author_facet Alrababah, Saif Addeen Ahmad Ali
author_sort Alrababah, Saif Addeen Ahmad Ali
collection USM
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.
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spelling usm.eprints-437412019-04-12T05:24:51Z http://eprints.usm.my/43741/ Multi Criteria Decision Making Approach For Product Aspect Extraction And Ranking In Aspect-Based Sentiment Analysis 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 NonPeerReviewed application/pdf en http://eprints.usm.my/43741/1/SAIF%20ADDEEN%20AHMAD%20ALI%20ALRABABAH.pdf Alrababah, Saif Addeen Ahmad Ali (2018) Multi Criteria Decision Making Approach For Product Aspect Extraction And Ranking In Aspect-Based Sentiment Analysis. PhD thesis, Universiti Sains Malaysia.
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
title 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_short 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
topic QA75.5-76.95 Electronic computers. Computer science
url http://eprints.usm.my/43741/1/SAIF%20ADDEEN%20AHMAD%20ALI%20ALRABABAH.pdf
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