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Comparative Analysis of Deep Learning Models for Predicting Online Review Helpfulness

Published: 19 June 2023 Publication History

Abstract

The exponential growth of online customer reviews has created challenges for potential buyers to filter and identify helpful reviews, directly affecting their shopping experience. Accurate prediction of review helpfulness can improve the selection and presentation of valuable reviews, leading to a better user experience and more informed purchasing decisions. To address the limitations of traditional machine learning methods that rely on handcrafted features and fail to capture semantic context, this paper presents a comparative analysis of existing deep learning models to predict the helpfulness of online reviews. Our study employs larger and more diverse datasets from three popular e-commerce platforms: TripAdvisor, Amazon, and Yelp, and compares multiple deep learning models, including Convolutional Neural Networks (CNN), Recurrent Neural Networks (RNN), and DistilBert, to identify the most accurate and effective predictions. Additionally, the study compares the deep learning models to the traditional machine learning algorithm XGBoost. Understanding the benefits and limitations of each model can lead to improved model selection and optimization, resulting in more accurate and efficient predictions for a wide range of applications. The results show that CNN consistently outperforms the other deep learning models and XGBoost regarding Mean Squared Error (MSE) and training time across all datasets.

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  • (2024)A novel deep learning model for detection of inconsistency in e-commerce websitesNeural Computing and Applications10.1007/s00521-024-09590-536:17(10339-10353)Online publication date: 16-Mar-2024

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CVIPPR '23: Proceedings of the 2023 Asia Conference on Computer Vision, Image Processing and Pattern Recognition
April 2023
93 pages
ISBN:9798400700033
DOI:10.1145/3596286
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 the author(s) 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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Association for Computing Machinery

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Published: 19 June 2023

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CVIPPR '23 Paper Acceptance Rate 14 of 38 submissions, 37%;
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  • (2024)A novel deep learning model for detection of inconsistency in e-commerce websitesNeural Computing and Applications10.1007/s00521-024-09590-536:17(10339-10353)Online publication date: 16-Mar-2024

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