short-paper

Helpfulness Prediction of Online Product Reviews

Authors:

Md. Enamul Haque,

Mehmet Engin Tozal,

Aminul IslamAuthors Info & Claims

DocEng '18: Proceedings of the ACM Symposium on Document Engineering 2018

Article No.: 35, Pages 1 - 4

https://doi.org/10.1145/3209280.3229105

Published: 28 August 2018 Publication History

Abstract

The simple question "Was this review helpful to you?" increases an estimated $2.7B revenue to Amazon.com annually 1. In this paper, we propose a solution to the problem of electronic product review accumulation using helpfulness prediction. The popularity of e-commerce and online retailers such as Amazon, eBay, Yelp, and TripAdvisor are largely relying on the presence of product reviews to attract more customers. The major issue for the user submitted reviews is to quantify and evaluate the actual effectiveness by combining all the reviews under a particular product. With the varying size of reviews for each product, it is quite cumbersome for the customers to get hold of the overall helpfulness.Therefore, we propose a feature extraction technique that can quantify and measure helpfulness for each product based on user submitted reviews.

References

[1]

Laura Connors, Susan M Mudambi, and David Schuff. 2011. Is it the review or the reviewer? A multi-method approach to determine the antecedents of online review helpfulness. In System Sciences (HICSS), 2011 44th Hawaii International Conference on. IEEE, 1--10.

Digital Library

[2]

Wenjing Duan, Bin Gu, and Andrew B Whinston. 2008. The dynamics of online word-of-mouth and product salesâĂŤAn empirical investigation of the movie industry. Journal of retailing 84, 2 (2008), 233--242.

[3]

Bin Fang, Qiang Ye, Deniz Kucukusta, and Rob Law. 2016. Analysis of the perceived value of online tourism reviews: Influence of readability and reviewer characteristics. Tourism Management 52 (2016), 498--506.

[4]

Rudolph Flesch. 1948. A new readability yardstick. Journal of applied psychology 32, 3 (1948), 221.

[5]

Anindya Ghose and Panagiotis G Ipeirotis. 2011. Estimating the helpfulness and economic impact of product reviews: Mining text and reviewer characteristics. Knowledge and Data Engineering, IEEE Transactions on 23, 10 (2011), 1498--1512.

Digital Library

[6]

Ruining He and Julian McAuley. 2016. Ups and downs: Modeling the visual evolution of fashion trends with one-class collaborative filtering. In proceedings of the 25th international conference on world wide web. International World Wide Web Conferences Steering Committee, 507--517.

Digital Library

[7]

Yu Hong, Jun Lu, Jianmin Yao, Qiaoming Zhu, and Guodong Zhou. 2012. What reviews are satisfactory: novel features for automatic helpfulness voting. In Proceedings of the 35th international ACM SIGIR conference on Research and development in information retrieval. ACM, 495--504.

Digital Library

[8]

Albert H Huang, Kuanchin Chen, David C Yen, and Trang P Tran. 2015. A study of factors that contribute to online review helpfulness. Computers in Human Behavior 48 (2015), 17--27.

Digital Library

[9]

Haijing Liu, Yang Gao, Pin Lv, Mengxue Li, Shiqiang Geng, Minglan Li, and Hao Wang. 2017. Using Argument-based Features to Predict and Analyse Review Helpfulness. arXiv preprint arXiv:1707.07279 (2017).

[10]

Yang Liu, Xiangji Huang, Aijun An, and Xiaohui Yu. 2007. ARSA: a sentiment-aware model for predicting sales performance using blogs. In Proceedings of the 30th annual international ACM SIGIR conference on Research and development in information retrieval. ACM, 607--614.

Digital Library

[11]

Yang Liu, Xiangji Huang, Aijun An, and Xiaohui Yu. 2008. Modeling and predicting the helpfulness of online reviews. In Data mining, 2008. ICDM'08. Eighth IEEE international conference on. IEEE, 443--452.

Digital Library

[12]

Tomas Mikolov, Kai Chen, Greg Corrado, and Jeffrey Dean. 2013. Efficient estimation of word representations in vector space. arXiv preprint arXiv:1301.3781 (2013).

[13]

Tomas Mikolov, Ilya Sutskever, Kai Chen, Greg S Corrado, and Jeff Dean. 2013. Distributed representations of words and phrases and their compositionality. In Advances in neural information processing systems. 3111--3119.

Digital Library

[14]

Susan M Mudambi and David Schuff. 2010. What makes a helpful review? A study of customer reviews on Amazon. com. MIS quarterly 34, 1 (2010), 185--200.

Digital Library

[15]

Ndapandula Nakashole and Tom M Mitchell. 2014. Language-Aware Truth Assessment of Fact Candidates. In ACL (1). 1009--1019.

[16]

Aika Qazi, Karim Bux Shah Syed, Ram Gopal Raj, Erik Cambria, Muhammad Tahir, and Daniyal Alghazzawi. 2016. A concept-level approach to the analysis of online review helpfulness. Computers in Human Behavior 58 (2016), 75--81.

Digital Library

[17]

Ruben Sipos, Arpita Ghosh, and Thorsten Joachims. 2014. Was this review helpful to you?: it depends! context and voting patterns in online content. In Proceedings of the 23rd international conference on World wide web. ACM, 337--348.

Digital Library

[18]

Yinfei Yang, Cen Chen, and Forrest Sheng Bao. 2016. Aspect-Based Helpfulness Prediction for Online Product Reviews. In Tools with Artificial Intelligence (ICTAI), 2016 IEEE 28th International Conference on. IEEE, 836--843.

[19]

Yinfei Yang, Yaowei Yan, Minghui Qiu, and Forrest Sheng Bao. {n. d.}. Semantic Analysis and Helpfulness Prediction of Text for Online Product Reviews. Volume 2: Short Papers ({n. d.}), 38.

Cited By

Malik MNawaz AJamjoom MIgnatov D(2024)Effectiveness of ELMo embeddings, and semantic models in predicting review helpfulnessIntelligent Data Analysis10.3233/IDA-23034928:4(1045-1065)Online publication date: 17-Jul-2024
https://doi.org/10.3233/IDA-230349
Sharma SSingh LTiwari R(2024)Design of an Efficient Integrated Feature Engineering based Deep Learning Model Using CNN for Customer’s Review Helpfulness PredictionWireless Personal Communications10.1007/s11277-023-10834-1133:4(2125-2161)Online publication date: 21-Feb-2024
https://doi.org/10.1007/s11277-023-10834-1
Li XLi QKim J(2023)A Review Helpfulness Modeling Mechanism for Online E-commerce: Multi-Channel CNN End‑to‑End ApproachApplied Artificial Intelligence10.1080/08839514.2023.216622637:1Online publication date: 12-Jan-2023
https://doi.org/10.1080/08839514.2023.2166226
Show More Cited By

Recommendations

Product-Aware Helpfulness Prediction of Online Reviews
WWW '19: The World Wide Web Conference

Helpful reviews are essential for e-commerce and review websites, as they can help customers make quick purchase decisions and merchants to increase profits. Due to a great number of online reviews with unknown helpfulness, it recently leads to promising ...
Can online product reviews be more helpful? Examining characteristics of information content by product type

Many online retailers and other product-oriented websites allow people to post product reviews for use by shoppers. While research indicates that these reviews influence consumers' shopping attitudes and behaviors, questions remain about how consumers ...
How to interpret the helpfulness of online product reviews: bridging the needs between customers and designers
SMUC '10: Proceedings of the 2nd international workshop on Search and mining user-generated contents

Helpful reviews are the valuable voice of the customer which benefit both consumers and product designers. On e-commerce websites, consumers are usually encouraged to rate whether a review is helpful or not. As consumers are not obligated to vote ...

Comments

Please enable JavaScript to view thecomments powered by Disqus.

Information & Contributors

Information

Published In

cover image ACM Conferences

DocEng '18: Proceedings of the ACM Symposium on Document Engineering 2018

August 2018

311 pages

ISBN:9781450357692

DOI:10.1145/3209280

Copyright © 2018 ACM.

Permission to make digital or hard copies of all or part of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for components of this work owned by others than ACM must be honored. Abstracting with credit is permitted. To copy otherwise, or republish, to post on servers or to redistribute to lists, requires prior specific permission and/or a fee. Request permissions from [email protected]

Sponsors

SIGWEB: ACM Special Interest Group on Hypertext, Hypermedia, and Web

In-Cooperation

SIGDOC: ACM Special Interest Group on Systems Documentation

Publisher

Association for Computing Machinery

New York, NY, United States

Publication History

Published: 28 August 2018

Permissions

Request permissions for this article.

Request Permissions

Check for updates

Author Tags

Qualifiers

Short-paper
Research
Refereed limited

Conference

DocEng '18

Sponsor:

SIGWEB

DocEng '18: ACM Symposium on Document Engineering 2018

August 28 - 31, 2018

NS, Halifax, Canada

Acceptance Rates

Overall Acceptance Rate 194 of 564 submissions, 34%

Contributors

Other Metrics

View Article Metrics

Bibliometrics & Citations

Bibliometrics

Article Metrics

15
Total Citations
View Citations
379
Total Downloads

Downloads (Last 12 months)42
Downloads (Last 6 weeks)5

Reflects downloads up to 26 Sep 2024

Other Metrics

View Author Metrics

Citations

Cited By

Malik MNawaz AJamjoom MIgnatov D(2024)Effectiveness of ELMo embeddings, and semantic models in predicting review helpfulnessIntelligent Data Analysis10.3233/IDA-23034928:4(1045-1065)Online publication date: 17-Jul-2024
https://doi.org/10.3233/IDA-230349
Sharma SSingh LTiwari R(2024)Design of an Efficient Integrated Feature Engineering based Deep Learning Model Using CNN for Customer’s Review Helpfulness PredictionWireless Personal Communications10.1007/s11277-023-10834-1133:4(2125-2161)Online publication date: 21-Feb-2024
https://doi.org/10.1007/s11277-023-10834-1
Li XLi QKim J(2023)A Review Helpfulness Modeling Mechanism for Online E-commerce: Multi-Channel CNN End‑to‑End ApproachApplied Artificial Intelligence10.1080/08839514.2023.216622637:1Online publication date: 12-Jan-2023
https://doi.org/10.1080/08839514.2023.2166226
Saptono RMine T(2022)Adaptive Neighborhood Distribution-based Model for Estimating Helpful Votes of Customer Review2022 IEEE/WIC/ACM International Joint Conference on Web Intelligence and Intelligent Agent Technology (WI-IAT)10.1109/WI-IAT55865.2022.00029(143-150)Online publication date: Nov-2022
https://doi.org/10.1109/WI-IAT55865.2022.00029
Zheng JWen PJi XLyu XYang Y(2021)Helpfulness Prediction of Online Drug Reviews2021 IEEE International Conference on Consumer Electronics and Computer Engineering (ICCECE)10.1109/ICCECE51280.2021.9342308(528-537)Online publication date: 15-Jan-2021
https://doi.org/10.1109/ICCECE51280.2021.9342308
Lee SLee KChoeh J(2020)Using Bayesian Network to Predict Online Review HelpfulnessSustainability10.3390/su1217699712:17(6997)Online publication date: 27-Aug-2020
https://doi.org/10.3390/su12176997
Haque MLing EIslam ATozal M(2020)Predicting Domain Specific Personal Attitudes and SentimentInternational Journal of Semantic Computing10.1142/S1793351X2040007314:02(199-222)Online publication date: 23-Sep-2020
https://doi.org/10.1142/S1793351X20400073
Saptono RMine T(2020)Time-based Sampling Methods for Detecting Helpful Reviews2020 IEEE/WIC/ACM International Joint Conference on Web Intelligence and Intelligent Agent Technology (WI-IAT)10.1109/WIIAT50758.2020.00076(508-513)Online publication date: Dec-2020
https://doi.org/10.1109/WIIAT50758.2020.00076
Bilal MMarjani MLali MMalik NGani AHashem I(2020)Profiling Users’ Behavior, and Identifying Important Features of Review “Helpfulness”IEEE Access10.1109/ACCESS.2020.29894638(77227-77244)Online publication date: 2020
https://doi.org/10.1109/ACCESS.2020.2989463
Lee EZhao H(2020)Deriving topic-related and interaction features to predict top attractive reviews for a specific business entityJournal of Business Analytics10.1080/2573234X.2020.1768808(1-15)Online publication date: 14-Jun-2020
https://doi.org/10.1080/2573234X.2020.1768808
Show More Cited By

View Options

Get Access

Login options

Check if you have access through your login credentials or your institution to get full access on this article.

Full Access

Get this Publication

View options

PDF

View or Download as a PDF file.

eReader

View online with eReader.

Media

Figures

Other

Tables

View Table of Contents