A survey of customer review helpfulness prediction techniques

Madeha Arif; Usman Qamar; Farhan Hassan Khan; Saba Bashir

doi:10.1007/978-3-030-01054-6_15

A survey of customer review helpfulness prediction techniques

Madeha Arif^*
, Usman Qamar
, Farhan Hassan Khan
, Saba Bashir

^*Corresponding author for this work

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › peer-review

6 Scopus citations

Abstract

Online user generated reviews are now a vital source of product evaluation to both consumer and retailer. There is a need of knowing the factors, generally affecting the helpfulness of reviews and how to identify them. Various studies and researches have been conducted for finding the helpfulness value of online reviews in past recent years. In this paper we have summarized and then analyzed the past work of review helpfulness prediction in a systematic way. The paper provides brief of methodology of each study and how it is contributing towards this domain. It also emphasizes on the pros of the methods used in past and how they are lacking in determining few other aspects of the review helpfulness. The survey discovers that the most popular techniques used for helpfulness prediction are supervised ones and most frequently used are Regression Models and SVM.

Original language	English
Title of host publication	Intelligent Systems and Applications - Proceedings of the 2018 Intelligent Systems Conference IntelliSys Volume 1
Editors	Kohei Arai, Supriya Kapoor, Rahul Bhatia
Publisher	Springer Verlag
Pages	215-226
Number of pages	12
ISBN (Print)	9783030010539
DOIs	https://doi.org/10.1007/978-3-030-01054-6_15
State	Published - 2018
Externally published	Yes
Event	Intelligent Systems Conference, IntelliSys 2018 - London, United Kingdom Duration: 6 Sep 2018 → 7 Sep 2018

Publication series

Name	Advances in Intelligent Systems and Computing
Volume	868
ISSN (Print)	2194-5357
ISSN (Electronic)	2194-5365

Conference

Conference	Intelligent Systems Conference, IntelliSys 2018
Country/Territory	United Kingdom
City	London
Period	6/09/18 → 7/09/18

Bibliographical note

Publisher Copyright:
© Springer Nature Switzerland AG 2019.

Keywords

Helpfulness
Helpfulness ratio
Product reviews
Reviewer history
WOM (Word of Mouth)

ASJC Scopus subject areas

Control and Systems Engineering
General Computer Science

Access to Document

10.1007/978-3-030-01054-6_15

Cite this

Arif, M., Qamar, U., Khan, F. H., & Bashir, S. (2018). A survey of customer review helpfulness prediction techniques. In K. Arai, S. Kapoor, & R. Bhatia (Eds.), Intelligent Systems and Applications - Proceedings of the 2018 Intelligent Systems Conference IntelliSys Volume 1 (pp. 215-226). (Advances in Intelligent Systems and Computing; Vol. 868). Springer Verlag. https://doi.org/10.1007/978-3-030-01054-6_15

Arif, Madeha ; Qamar, Usman ; Khan, Farhan Hassan et al. / A survey of customer review helpfulness prediction techniques. Intelligent Systems and Applications - Proceedings of the 2018 Intelligent Systems Conference IntelliSys Volume 1. editor / Kohei Arai ; Supriya Kapoor ; Rahul Bhatia. Springer Verlag, 2018. pp. 215-226 (Advances in Intelligent Systems and Computing).

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title = "A survey of customer review helpfulness prediction techniques",

abstract = "Online user generated reviews are now a vital source of product evaluation to both consumer and retailer. There is a need of knowing the factors, generally affecting the helpfulness of reviews and how to identify them. Various studies and researches have been conducted for finding the helpfulness value of online reviews in past recent years. In this paper we have summarized and then analyzed the past work of review helpfulness prediction in a systematic way. The paper provides brief of methodology of each study and how it is contributing towards this domain. It also emphasizes on the pros of the methods used in past and how they are lacking in determining few other aspects of the review helpfulness. The survey discovers that the most popular techniques used for helpfulness prediction are supervised ones and most frequently used are Regression Models and SVM.",

keywords = "Helpfulness, Helpfulness ratio, Product reviews, Reviewer history, WOM (Word of Mouth)",

author = "Madeha Arif and Usman Qamar and Khan, \{Farhan Hassan\} and Saba Bashir",

note = "Publisher Copyright: {\textcopyright} Springer Nature Switzerland AG 2019.; Intelligent Systems Conference, IntelliSys 2018 ; Conference date: 06-09-2018 Through 07-09-2018",

year = "2018",

doi = "10.1007/978-3-030-01054-6\_15",

language = "English",

isbn = "9783030010539",

series = "Advances in Intelligent Systems and Computing",

publisher = "Springer Verlag",

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Arif, M, Qamar, U, Khan, FH & Bashir, S 2018, A survey of customer review helpfulness prediction techniques. in K Arai, S Kapoor & R Bhatia (eds), Intelligent Systems and Applications - Proceedings of the 2018 Intelligent Systems Conference IntelliSys Volume 1. Advances in Intelligent Systems and Computing, vol. 868, Springer Verlag, pp. 215-226, Intelligent Systems Conference, IntelliSys 2018, London, United Kingdom, 6/09/18. https://doi.org/10.1007/978-3-030-01054-6_15

A survey of customer review helpfulness prediction techniques. / Arif, Madeha; Qamar, Usman; Khan, Farhan Hassan et al.
Intelligent Systems and Applications - Proceedings of the 2018 Intelligent Systems Conference IntelliSys Volume 1. ed. / Kohei Arai; Supriya Kapoor; Rahul Bhatia. Springer Verlag, 2018. p. 215-226 (Advances in Intelligent Systems and Computing; Vol. 868).

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › peer-review

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AU - Qamar, Usman

AU - Khan, Farhan Hassan

AU - Bashir, Saba

N1 - Publisher Copyright: © Springer Nature Switzerland AG 2019.

PY - 2018

Y1 - 2018

N2 - Online user generated reviews are now a vital source of product evaluation to both consumer and retailer. There is a need of knowing the factors, generally affecting the helpfulness of reviews and how to identify them. Various studies and researches have been conducted for finding the helpfulness value of online reviews in past recent years. In this paper we have summarized and then analyzed the past work of review helpfulness prediction in a systematic way. The paper provides brief of methodology of each study and how it is contributing towards this domain. It also emphasizes on the pros of the methods used in past and how they are lacking in determining few other aspects of the review helpfulness. The survey discovers that the most popular techniques used for helpfulness prediction are supervised ones and most frequently used are Regression Models and SVM.

AB - Online user generated reviews are now a vital source of product evaluation to both consumer and retailer. There is a need of knowing the factors, generally affecting the helpfulness of reviews and how to identify them. Various studies and researches have been conducted for finding the helpfulness value of online reviews in past recent years. In this paper we have summarized and then analyzed the past work of review helpfulness prediction in a systematic way. The paper provides brief of methodology of each study and how it is contributing towards this domain. It also emphasizes on the pros of the methods used in past and how they are lacking in determining few other aspects of the review helpfulness. The survey discovers that the most popular techniques used for helpfulness prediction are supervised ones and most frequently used are Regression Models and SVM.

KW - Helpfulness

KW - Helpfulness ratio

KW - Product reviews

KW - Reviewer history

KW - WOM (Word of Mouth)

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BT - Intelligent Systems and Applications - Proceedings of the 2018 Intelligent Systems Conference IntelliSys Volume 1

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Arif M, Qamar U, Khan FH, Bashir S. A survey of customer review helpfulness prediction techniques. In Arai K, Kapoor S, Bhatia R, editors, Intelligent Systems and Applications - Proceedings of the 2018 Intelligent Systems Conference IntelliSys Volume 1. Springer Verlag. 2018. p. 215-226. (Advances in Intelligent Systems and Computing). doi: 10.1007/978-3-030-01054-6_15

A survey of customer review helpfulness prediction techniques

Abstract

Publication series

Conference

Bibliographical note

Keywords

ASJC Scopus subject areas

Access to Document

Fingerprint

Cite this