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Temporal Model of the Online Customer Review Helpfulness Prediction

Published: 31 July 2017 Publication History

Abstract

Customer reviews provide opinions and relevant information that will affect the purchase behavior of other customers. Many studies focused on the prediction of the helpfulness of customer reviews to find the helpful reviews, which are traditionally determined by the helpful voting results. In our study, we find that the voting result of an online review is not a constant over time. Therefore, predicting the voting result based on the analysis of text is not enough; the temporal issue must be considered. We propose a system that can rank the reviews based on a set of linguistic features with a linear regression model. To evaluate our system, we collect Chinese custom reviews in eight product categories (books, digital cameras, tablet PC, backpacks, movies, men shoes, toys and cell phones) from Amazon.cn with the voting result on the helpfulness of the reviews. Since the voting result may be affected by voting time and total voting number, we define a new evaluation index and compare the regression results. The results show that the system has less prediction error when it takes the time information into the prediction model.

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Cited By

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  • (2020)Cross-Domain Helpfulness Prediction of Online Consumer Reviews by Deep Learning Model2020 IEEE 21st International Conference on Information Reuse and Integration for Data Science (IRI)10.1109/IRI49571.2020.00069(412-418)Online publication date: Aug-2020
  • (2019)Integrating neural and syntactic features on the helpfulness analysis of the online customer reviewsProceedings of the 2019 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining10.1145/3341161.3344825(1013-1017)Online publication date: 27-Aug-2019

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cover image ACM Conferences
ASONAM '17: Proceedings of the 2017 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining 2017
July 2017
698 pages
ISBN:9781450349932
DOI:10.1145/3110025
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]

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Publication History

Published: 31 July 2017

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Author Tags

  1. Helpfulness prediction
  2. Linear Regression
  3. Online Customer review

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View all
  • (2020)Cross-Domain Helpfulness Prediction of Online Consumer Reviews by Deep Learning Model2020 IEEE 21st International Conference on Information Reuse and Integration for Data Science (IRI)10.1109/IRI49571.2020.00069(412-418)Online publication date: Aug-2020
  • (2019)Integrating neural and syntactic features on the helpfulness analysis of the online customer reviewsProceedings of the 2019 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining10.1145/3341161.3344825(1013-1017)Online publication date: 27-Aug-2019

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