Dynamic Text Augmentation for Robust Sentiment Analysis: Enhancing Model Performance With EDA and Multi-Channel CNN
The rapid growth of social media has revolutionized the way individuals share and access opinions, generating vast amounts of textual data containing diverse sentiments. Sentiment analysis enables organizations to derive valuable insights from this data, facilitating improved decision-making. Howeve...
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| Format: | Article |
| Language: | English |
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IEEE
2025-01-01
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| Series: | IEEE Access |
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| Online Access: | https://ieeexplore.ieee.org/document/10870205/ |
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| author | Komang Wahyu Trisna Jinjie Huang Yuanjian Chen I Gede Juliana Eka Putra |
| author_facet | Komang Wahyu Trisna Jinjie Huang Yuanjian Chen I Gede Juliana Eka Putra |
| author_sort | Komang Wahyu Trisna |
| collection | DOAJ |
| description | The rapid growth of social media has revolutionized the way individuals share and access opinions, generating vast amounts of textual data containing diverse sentiments. Sentiment analysis enables organizations to derive valuable insights from this data, facilitating improved decision-making. However, the limited availability of labeled datasets and challenges related to data imbalance often hinder the development of robust sentiment analysis models. In this study, we propose a novel framework dynamic Easy Data Augmentation (EDA) technique with a Multi-Channel Convolutional Neural Network (MCNN) to address these issues. The EDA techniques including synonym replacement (SR), random insertion (RI), random deletion (RD), and random swap (RS), enrich the training data by introducing diversity, thereby enhancing model robustness. The MCNN architecture effectively captures local patterns in text using multiple filter sizes, enabling comprehensive feature extraction with low computational overhead. Furthermore, the integration of Word2Vec embeddings ensures meaningful text representation, leveraging context-independent word relationships for improved sentiment analysis. Experimental evaluations demonstrate the effectiveness of the proposed approach, highlighting significant improvements in F1-score compared to baseline models. The findings underscore the potential of dynamic text augmentation and efficient deep learning architectures to overcome the challenges of limited training data, offering a scalable solution for sentiment analysis in real-world applications. Additionally, the proposed model achieves an average performance improvement of 11.34% over previous models. |
| format | Article |
| id | doaj-art-403d272c870146ada56d0ad87bbf9fa3 |
| institution | DOAJ |
| issn | 2169-3536 |
| language | English |
| publishDate | 2025-01-01 |
| publisher | IEEE |
| record_format | Article |
| series | IEEE Access |
| spelling | doaj-art-403d272c870146ada56d0ad87bbf9fa32025-08-20T03:00:25ZengIEEEIEEE Access2169-35362025-01-0113319783199110.1109/ACCESS.2025.353862110870205Dynamic Text Augmentation for Robust Sentiment Analysis: Enhancing Model Performance With EDA and Multi-Channel CNNKomang Wahyu Trisna0https://orcid.org/0000-0002-0543-5105Jinjie Huang1https://orcid.org/0000-0001-9243-3011Yuanjian Chen2https://orcid.org/0000-0003-0718-7405I Gede Juliana Eka Putra3School of Computer Science and Technology, Harbin University of Science and Technology, Harbin, ChinaSchool of Computer Science and Technology, Harbin University of Science and Technology, Harbin, ChinaSchool of Computer Science and Technology, Harbin University of Science and Technology, Harbin, ChinaDepartment of Informatic Engineering, Primakara University, Denpasar, Bali, IndonesiaThe rapid growth of social media has revolutionized the way individuals share and access opinions, generating vast amounts of textual data containing diverse sentiments. Sentiment analysis enables organizations to derive valuable insights from this data, facilitating improved decision-making. However, the limited availability of labeled datasets and challenges related to data imbalance often hinder the development of robust sentiment analysis models. In this study, we propose a novel framework dynamic Easy Data Augmentation (EDA) technique with a Multi-Channel Convolutional Neural Network (MCNN) to address these issues. The EDA techniques including synonym replacement (SR), random insertion (RI), random deletion (RD), and random swap (RS), enrich the training data by introducing diversity, thereby enhancing model robustness. The MCNN architecture effectively captures local patterns in text using multiple filter sizes, enabling comprehensive feature extraction with low computational overhead. Furthermore, the integration of Word2Vec embeddings ensures meaningful text representation, leveraging context-independent word relationships for improved sentiment analysis. Experimental evaluations demonstrate the effectiveness of the proposed approach, highlighting significant improvements in F1-score compared to baseline models. The findings underscore the potential of dynamic text augmentation and efficient deep learning architectures to overcome the challenges of limited training data, offering a scalable solution for sentiment analysis in real-world applications. Additionally, the proposed model achieves an average performance improvement of 11.34% over previous models.https://ieeexplore.ieee.org/document/10870205/Convolutional neural networksdeep learningsentiment analysistext augmentationword embedding |
| spellingShingle | Komang Wahyu Trisna Jinjie Huang Yuanjian Chen I Gede Juliana Eka Putra Dynamic Text Augmentation for Robust Sentiment Analysis: Enhancing Model Performance With EDA and Multi-Channel CNN IEEE Access Convolutional neural networks deep learning sentiment analysis text augmentation word embedding |
| title | Dynamic Text Augmentation for Robust Sentiment Analysis: Enhancing Model Performance With EDA and Multi-Channel CNN |
| title_full | Dynamic Text Augmentation for Robust Sentiment Analysis: Enhancing Model Performance With EDA and Multi-Channel CNN |
| title_fullStr | Dynamic Text Augmentation for Robust Sentiment Analysis: Enhancing Model Performance With EDA and Multi-Channel CNN |
| title_full_unstemmed | Dynamic Text Augmentation for Robust Sentiment Analysis: Enhancing Model Performance With EDA and Multi-Channel CNN |
| title_short | Dynamic Text Augmentation for Robust Sentiment Analysis: Enhancing Model Performance With EDA and Multi-Channel CNN |
| title_sort | dynamic text augmentation for robust sentiment analysis enhancing model performance with eda and multi channel cnn |
| topic | Convolutional neural networks deep learning sentiment analysis text augmentation word embedding |
| url | https://ieeexplore.ieee.org/document/10870205/ |
| work_keys_str_mv | AT komangwahyutrisna dynamictextaugmentationforrobustsentimentanalysisenhancingmodelperformancewithedaandmultichannelcnn AT jinjiehuang dynamictextaugmentationforrobustsentimentanalysisenhancingmodelperformancewithedaandmultichannelcnn AT yuanjianchen dynamictextaugmentationforrobustsentimentanalysisenhancingmodelperformancewithedaandmultichannelcnn AT igedejulianaekaputra dynamictextaugmentationforrobustsentimentanalysisenhancingmodelperformancewithedaandmultichannelcnn |