DC-BiLSTM-CNN Algorithm for Sentiment Analysis of Chinese Product Reviews

The rapid growth of e-commerce has led to a significant increase in user feedback, especially in the form of post-purchase comments on online platforms. These reviews not only reflect customer sentiments but also crucially influence other users’ purchasing decisions due to their public accessibility...

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Main Authors: Yuanfang Dong, Xiaofei Li, Meiling He, Jun Li
Format: Article
Language:English
Published: Taylor & Francis Group 2025-12-01
Series:Applied Artificial Intelligence
Online Access:https://www.tandfonline.com/doi/10.1080/08839514.2025.2461809
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author Yuanfang Dong
Xiaofei Li
Meiling He
Jun Li
author_facet Yuanfang Dong
Xiaofei Li
Meiling He
Jun Li
author_sort Yuanfang Dong
collection DOAJ
description The rapid growth of e-commerce has led to a significant increase in user feedback, especially in the form of post-purchase comments on online platforms. These reviews not only reflect customer sentiments but also crucially influence other users’ purchasing decisions due to their public accessibility. The sheer volume and complexity of product reviews make manual sorting challenging, necessitating businesses to autonomously process and discern customer sentiments. Chinese, a predominant language on e-commerce platforms, presents unique challenges in sentiment analysis due to its character-based nature. This paper introduces an innovative Dual-Channel BiLSTM-CNN (DC-BiLSTM-CNN) algorithm. Based on the language characteristics of Chinese product reviews, a sentiment analysis algorithm, dual channel BiLSTM-CNN (DC-BiLSTM-CNN), is proposed. The algorithm constructs two channels, transforming text into both character and word vectors and inputting them into Bidirectional Long Short-Term Memory (BiLSTM), and Convolutional Neural Network (CNN) models. The combination of these channels facilitates a more comprehensive feature extraction from reviews. Comparative analysis revealed that DC-BiLSTM-CNN significantly outperforms baseline models, substantially enhancing the classification of product reviews. We conclude that the proposed DC-BiLSTM-CNN algorithm offers an effective solution for handling Chinese product reviews, carrying positive implications for businesses seeking to enhance product and service quality, ultimately resulting in heightened user satisfaction.
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institution Kabale University
issn 0883-9514
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language English
publishDate 2025-12-01
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record_format Article
series Applied Artificial Intelligence
spelling doaj-art-b46131dab3fc4d028f3fb9e36f4c3f8d2025-02-05T11:45:53ZengTaylor & Francis GroupApplied Artificial Intelligence0883-95141087-65452025-12-0139110.1080/08839514.2025.2461809DC-BiLSTM-CNN Algorithm for Sentiment Analysis of Chinese Product ReviewsYuanfang Dong0Xiaofei Li1Meiling He2Jun Li3School of Economics and Management, Changchun University of Science and Technology, Changchun, ChinaSchool of Economics and Management, Changchun University of Science and Technology, Changchun, ChinaSchool of Economics and Management, Changchun University of Science and Technology, Changchun, ChinaDepartment of Data Science, Jilin University of Finance and Economics, Changchun, ChinaThe rapid growth of e-commerce has led to a significant increase in user feedback, especially in the form of post-purchase comments on online platforms. These reviews not only reflect customer sentiments but also crucially influence other users’ purchasing decisions due to their public accessibility. The sheer volume and complexity of product reviews make manual sorting challenging, necessitating businesses to autonomously process and discern customer sentiments. Chinese, a predominant language on e-commerce platforms, presents unique challenges in sentiment analysis due to its character-based nature. This paper introduces an innovative Dual-Channel BiLSTM-CNN (DC-BiLSTM-CNN) algorithm. Based on the language characteristics of Chinese product reviews, a sentiment analysis algorithm, dual channel BiLSTM-CNN (DC-BiLSTM-CNN), is proposed. The algorithm constructs two channels, transforming text into both character and word vectors and inputting them into Bidirectional Long Short-Term Memory (BiLSTM), and Convolutional Neural Network (CNN) models. The combination of these channels facilitates a more comprehensive feature extraction from reviews. Comparative analysis revealed that DC-BiLSTM-CNN significantly outperforms baseline models, substantially enhancing the classification of product reviews. We conclude that the proposed DC-BiLSTM-CNN algorithm offers an effective solution for handling Chinese product reviews, carrying positive implications for businesses seeking to enhance product and service quality, ultimately resulting in heightened user satisfaction.https://www.tandfonline.com/doi/10.1080/08839514.2025.2461809
spellingShingle Yuanfang Dong
Xiaofei Li
Meiling He
Jun Li
DC-BiLSTM-CNN Algorithm for Sentiment Analysis of Chinese Product Reviews
Applied Artificial Intelligence
title DC-BiLSTM-CNN Algorithm for Sentiment Analysis of Chinese Product Reviews
title_full DC-BiLSTM-CNN Algorithm for Sentiment Analysis of Chinese Product Reviews
title_fullStr DC-BiLSTM-CNN Algorithm for Sentiment Analysis of Chinese Product Reviews
title_full_unstemmed DC-BiLSTM-CNN Algorithm for Sentiment Analysis of Chinese Product Reviews
title_short DC-BiLSTM-CNN Algorithm for Sentiment Analysis of Chinese Product Reviews
title_sort dc bilstm cnn algorithm for sentiment analysis of chinese product reviews
url https://www.tandfonline.com/doi/10.1080/08839514.2025.2461809
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AT xiaofeili dcbilstmcnnalgorithmforsentimentanalysisofchineseproductreviews
AT meilinghe dcbilstmcnnalgorithmforsentimentanalysisofchineseproductreviews
AT junli dcbilstmcnnalgorithmforsentimentanalysisofchineseproductreviews