融合图像与文本的多模态情感分析方法研究 | |
徐楠![]() | |
Subtype | 博士 |
Thesis Advisor | 毛文吉 |
2020-05-30 | |
Degree Grantor | 中国科学院大学 |
Place of Conferral | 中国科学院自动化研究所 |
Degree Name | 工学博士 |
Degree Discipline | 计算机应用技术 |
Keyword | 多模态情感分析 图像语义 信息交互 属性级 多模态数据增强 |
Abstract | 随着互联网的发展,社交媒体已经成为用户发表个人观点、分享信息及表达情感的重要平台。情感分析作为社交媒体分析的一个重要研究课题,是舆情监测、口碑营销、商品推荐等诸多实际问题的基础,在公共安全、商业等领域具有重要的研究意义和应用价值。 随着多模态的社交媒体平台如抖音、Instagram等的普及,社交媒体数据形式呈现多模态趋势(如文本、图像等),提升了用户获取信息的效率。但是,由于多模态数据的复杂性和异构性,使计算机在多模态数据内容理解上更加困难,为传统的基于文本的情感分析任务带来了新的挑战。本论文研究 融合图像与文本的多模态情感分析方法,从多模态数据表征层、特征融合层、属性层及数据处理层多个角度,聚焦多模态数据之间的语义关联、信息交互、细粒度的属性级情感建模及多模态数据增强等研究问题,以增强计算机对多模态数据的感知能力,提升多模态情感分析模型的性能。 本论文的主要研究贡献包括:
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Other Abstract | With the development of the Internet, social media has become an important platform for users to publish personal opinions, share information and express sentiments. As an important research field of social media analysis, sentiment analysis is the basis of practical problems such as public opinion monitoring, word-of-mouth marketing, commodity recommendation, etc. It has high research and application values in the fields of public security, business and so on. With the widespread of multimodal social media platforms such as Tiktok and Instagram, the multimodal form of social media data, including text, image, etc., has improved the efficiency of users' access to information. However, it is more difficult for computer to understand the content of multimodal data due to the complexity and heterogeneity of multimodal data, which brings new challenges to the traditional text-based sentiment analysis task. This thesis focuses on the problem of multimodal sentiment analysis for image and text data. This thesis studies the multimodal semantic association, information interaction, fine-grained aspect based sentiment modeling and multimodal data augmentation at multimodal data representation, feature fusion, aspect level and data processing. This thesis aims to enhance the perception ability of the computer to multimodal data and improve the performance of multimodal sentiment analysis model. The main research contributions of this thesis are as follows:
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Pages | 126 |
Language | 中文 |
Document Type | 学位论文 |
Identifier | http://ir.ia.ac.cn/handle/173211/39149 |
Collection | 复杂系统管理与控制国家重点实验室_互联网大数据与信息安全 |
Corresponding Author | 徐楠 |
Recommended Citation GB/T 7714 | 徐楠. 融合图像与文本的多模态情感分析方法研究[D]. 中国科学院自动化研究所. 中国科学院大学,2020. |
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