國立高雄大學圖資館 |

登入

回首頁

Improving Detection Accuracy for Fake Customer Product Reviews.

紀錄類型:	書目-電子資源 : Monograph/item
正題名/作者:	Improving Detection Accuracy for Fake Customer Product Reviews.
作者:	Ahmad, Waqas.
出版者:	Ann Arbor : ProQuest Dissertations & Theses, 2022
面頁冊數:	112 p.
附註:	Source: Dissertations Abstracts International, Volume: 83-06, Section: B.
附註:	Advisor: Sarkani, Shahryar;Fossaceca, John.
Contained By:	Dissertations Abstracts International83-06B.
標題:	Engineering.
電子資源:	http://pqdd.sinica.edu.tw/twdaoapp/servlet/advanced?query=28865477
ISBN:	9798759949961

Improving Detection Accuracy for Fake Customer Product Reviews.
Ahmad, Waqas.

Improving Detection Accuracy for Fake Customer Product Reviews. - Ann Arbor : ProQuest Dissertations & Theses, 2022 - 112 p.

Source: Dissertations Abstracts International, Volume: 83-06, Section: B.

Thesis (D.Engr.)--The George Washington University, 2022.

This item must not be sold to any third party vendors.

Online reviews are an important tool for guiding consumers’ eCommerce purchasing decisions. The eCommerce industry has seen explosive growth in online reviews for products and services have multiplied accordingly. Intense competition for the consumer dollar has led to an influx of fake reviews to lure or discourage potential buyers. By falsely influencing buying decisions, fake reviews result in dissatisfied customers, costly returns, and billions of dollars in wasted consumer spending. The ability to detect fake reviews with a high degree of accuracy is important to both online merchants and consumers but has posed technical challenges over the years. Current practices use machine learning (ML) models to analyze and classify review text. The detection accuracy of these methods is at best 87% and is sensitive to the inputs to the ML programs. In this research, natural language processing techniques were first used to preprocess the reviews and then ML models, including those using deep learning methods, were used to classify them. This research provides an improved method for increasing detection accuracy of fake reviews from 87% up to 89% compared to existing classification models, and our experiments demonstrate the efficacy and improved accuracy of the new model in identifying fake reviews.

ISBN: 9798759949961Subjects--Topical Terms:

210888
Engineering.
Subjects--Index Terms:

Machine learning

Improving Detection Accuracy for Fake Customer Product Reviews.
LDR:02534nmm a2200397 4500 001 616503
005 20220513114354.5
008 220920s2022 ||||||||||||||||| ||eng d
020 $a 9798759949961
035 $a (MiAaPQ)AAI28865477
035 $a AAI28865477
040 $a MiAaPQ $c MiAaPQ
100 1 $a Ahmad, Waqas. $3 915882
245 1 0 $a Improving Detection Accuracy for Fake Customer Product Reviews.
260 1 $a Ann Arbor : $b ProQuest Dissertations & Theses, $c 2022
300 $a 112 p.
500 $a Source: Dissertations Abstracts International, Volume: 83-06, Section: B.
500 $a Advisor: Sarkani, Shahryar;Fossaceca, John.
502 $a Thesis (D.Engr.)--The George Washington University, 2022.
506 $a This item must not be sold to any third party vendors.
520 $a Online reviews are an important tool for guiding consumers’ eCommerce purchasing decisions. The eCommerce industry has seen explosive growth in online reviews for products and services have multiplied accordingly. Intense competition for the consumer dollar has led to an influx of fake reviews to lure or discourage potential buyers. By falsely influencing buying decisions, fake reviews result in dissatisfied customers, costly returns, and billions of dollars in wasted consumer spending. The ability to detect fake reviews with a high degree of accuracy is important to both online merchants and consumers but has posed technical challenges over the years. Current practices use machine learning (ML) models to analyze and classify review text. The detection accuracy of these methods is at best 87% and is sensitive to the inputs to the ML programs. In this research, natural language processing techniques were first used to preprocess the reviews and then ML models, including those using deep learning methods, were used to classify them. This research provides an improved method for increasing detection accuracy of fake reviews from 87% up to 89% compared to existing classification models, and our experiments demonstrate the efficacy and improved accuracy of the new model in identifying fake reviews.
590 $a School code: 0075.
650 4 $a Engineering. $3 210888
650 4 $a Web studies. $3 708690
650 4 $a Computer science. $3 199325
650 4 $a Artificial intelligence. $3 194058
653 $a Machine learning
653 $a Online reviews
653 $a Natural language processing
653 $a Deep learning
690 $a 0537
690 $a 0984
690 $a 0800
690 $a 0646
690 $a 0338
690 $a 0505
710 2 $a The George Washington University. $b Engineering Management. $3 857371
773 0 $t Dissertations Abstracts International $g 83-06B.
790 $a 0075
791 $a D.Engr.
792 $a 2022
793 $a English
856 4 0 $u http://pqdd.sinica.edu.tw/twdaoapp/servlet/advanced?query=28865477