Sentiment analysis methods, applications, and challenges: A systematic literature review

Journal of King Saud University - Computer and Information Sciences, Год журнала: 2024, Номер 36(4), С. 102048 - 102048

Опубликована: Апрель 1, 2024

Язык: Английский

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Graph convolutional network based on self-attention variational autoencoder and capsule contrastive learning for aspect-based sentiment analysis

Expert Systems with Applications, Год журнала: 2025, Номер unknown, С. 127172 - 127172

Опубликована: Март 1, 2025

Язык: Английский

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Semantic enhancement and cross-modal interaction fusion for sentiment analysis in social media

PLoS ONE, Год журнала: 2025, Номер 20(4), С. e0321011 - e0321011

Опубликована: Апрель 28, 2025

The rapid development of social media has significantly impacted sentiment analysis, essential for understanding public opinion and predicting trends. However, modality fusion in analysis can introduce a lot noise because the differences semantic representations among various modalities, ultimately impacting accuracy classification results. Thus, this paper presents Semantic Enhancement Cross-Modal Interaction Fusion (SECIF) model to address these issues. Firstly, BERT ResNet extract feature from text images. Secondly, GMHA mechanism is proposed aggregate important information mitigate influence noise. Then, an ICN module created capture complex contextual dependencies enhance capability representations. Finally, cross-modal interaction implemented. Text features are considered primary, image auxiliary, enabling profound integration textual visual features. model's performance optimized by combining cross-entropy KL divergence losses. experiments conducted using dataset collected events on Sina Weibo. results demonstrate that outperforms comparison models. SECIF improves 11.19%, 82.27%, 4.83% over average text-only, image-only, multimodal models, respectively. compared with ten baseline models publicly available datasets. experimental show 4.70% F1 score 6.56%. Through governments better understand emotions trends, facilitating more targeted effective management strategies.

Язык: Английский

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Dual-channel relative position guided attention networks for aspect-based sentiment analysis

Expert Systems with Applications, Год журнала: 2024, Номер 253, С. 124271 - 124271

Опубликована: Май 18, 2024

Язык: Английский

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Investigating the impact of sentiments on stock market using digital proxies: Current trends, challenges, and future directions

Expert Systems with Applications, Год журнала: 2025, Номер unknown, С. 127864 - 127864

Опубликована: Май 1, 2025

Язык: Английский

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Parameter-efficient online knowledge distillation for pretrained language models

Expert Systems with Applications, Год журнала: 2024, Номер 265, С. 126040 - 126040

Опубликована: Дек. 9, 2024

Язык: Английский

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Assessing a BERT-based model for analyzing subjectivity and classifying academic articles

Multimedia Tools and Applications, Год журнала: 2024, Номер unknown

Опубликована: Июнь 17, 2024

Язык: Английский

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FITE-GAT: Enhancing aspect-level sentiment classification with FT-RoBERTa induced trees and graph attention network

Expert Systems with Applications, Год журнала: 2024, Номер unknown, С. 125890 - 125890

Опубликована: Ноя. 1, 2024

Язык: Английский

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Application Domains of Aspect and Sentiment Classification Techniques: A Survey

Neurocomputing, Год журнала: 2024, Номер unknown, С. 129237 - 129237

Опубликована: Дек. 1, 2024

Язык: Английский

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