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...
Saved in:
| Main Authors: | , , , |
|---|---|
| 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 |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
| _version_ | 1832098349481721856 |
|---|---|
| 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. |
| format | Article |
| id | doaj-art-b46131dab3fc4d028f3fb9e36f4c3f8d |
| institution | Kabale University |
| issn | 0883-9514 1087-6545 |
| language | English |
| publishDate | 2025-12-01 |
| publisher | Taylor & Francis Group |
| 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 |
| work_keys_str_mv | AT yuanfangdong dcbilstmcnnalgorithmforsentimentanalysisofchineseproductreviews AT xiaofeili dcbilstmcnnalgorithmforsentimentanalysisofchineseproductreviews AT meilinghe dcbilstmcnnalgorithmforsentimentanalysisofchineseproductreviews AT junli dcbilstmcnnalgorithmforsentimentanalysisofchineseproductreviews |