Enhancing Weibo Sentiment Analysis With Multi-Modal Learning: Integrating Text and Synthesized Images With Contrastive Learning
In this paper, we aim to improve sentiment analysis on weibo, a vital platform for sentiment and opinion expression. Current sentiment analysis methods struggle with the unique challenges of Weibo posts, which often contain informal language, sarcasm, and lack of context, making it difficult to capt...
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| Main Authors: | , , , |
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| Format: | Article |
| Language: | English |
| Published: |
IEEE
2025-01-01
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| Series: | IEEE Access |
| Subjects: | |
| Online Access: | https://ieeexplore.ieee.org/document/11036745/ |
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| Summary: | In this paper, we aim to improve sentiment analysis on weibo, a vital platform for sentiment and opinion expression. Current sentiment analysis methods struggle with the unique challenges of Weibo posts, which often contain informal language, sarcasm, and lack of context, making it difficult to capture complex emotions. Additionally, existing text-based models fail to account for visual cues and rely heavily on predefined emotion categories, limiting their ability to detect nuanced sentiments. To address these issues, we propose a novel multimodal approach that integrates both textual and visual elements, leveraging a pretrained text-to-image model to generate images from posts. Integrated with a self-supervised contrastive learning objective, this approach empowers our model to extract knowledge from unlabeled data and achieve enhanced comprehension of nuanced emotional cues. Our method enhances sentiment analysis on microblogs by introducing a framework that captures both text and synthesized visual cues, enabling deeper sentiment interpretation. By incorporating self-supervised learning, we significantly improve the detection of complex emotions, particularly those related to mental health, offering a more comprehensive solution for sentiment analysis on social platforms. The innovation of this method lies in the introduction of synthetic images and self-supervised contrastive learning into microblog sentiment analysis, and the accuracy and generalization ability of the model are significantly improved through multimodal feature fusion and contrastive learning. |
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| ISSN: | 2169-3536 |