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It employs a text-guided spatial attention mechanism, fusing textual and visual features to discern subtle emotional interplays. The model’s end-to-end learning approach and attention modules contribute to its efficacy in capturing nuanced sentiment expressions. Empirical evaluations on established multimodal sentiment datasets, MVSA-Single and MVSA-Multi, validate the proposed methodology’s effectiveness. Comparative analyses with state-of-the-art models showcase the superior performance of our approach, with an accuracy improvement of 6% on MVSA-Single and 1.6% on MVSA-Multi. This research significantly contributes to optimizing SA in social media data by offering a versatile and potent framework for data management and analysis. 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引用次数: 0
摘要
在快速发展的社交媒体环境中,对多模态数据进行精确情感分析(SA)的需求变得越来越重要。本文介绍了一种为高效多模态情感特征提取量身定制的复杂数据湖架构,以应对不同数据类型带来的挑战。所提出的框架包括一个强大的存储解决方案和一个创新的 SA 模型--多级空间注意力融合(MLSAF),该模型善于同时处理文本和视觉数据。数据湖架构由五层组成,便于实时和离线数据收集、存储、处理、标准化接口服务和数据挖掘分析。集成到数据湖架构中的 MLSAF 模型采用了一种新颖的 SA 方法。它采用文本引导的空间注意力机制,融合文本和视觉特征来辨别微妙的情感交织。该模型的端到端学习方法和注意力模块有助于有效捕捉细微的情感表达。在已建立的多模态情感数据集 MVSA-Single 和 MVSA-Multi 上进行的实证评估验证了所提出方法的有效性。与最先进模型的对比分析表明,我们的方法性能优越,在 MVSA-Single 和 MVSA-Multi 数据集上的准确率分别提高了 6% 和 1.6%。这项研究为数据管理和分析提供了一个多功能的有效框架,为优化社交媒体数据中的 SA 做出了重大贡献。将 MLSAF 与可扩展的数据湖架构整合在一起,是一项战略性创新,有助于驾驭社交媒体数据分析不断变化的复杂性。
Design and Efficacy of a Data Lake Architecture for Multimodal Emotion Feature Extraction in Social Media
In the rapidly evolving landscape of social media, the demand for precise sentiment analysis (SA) on multimodal data has become increasingly pivotal. This paper introduces a sophisticated data lake architecture tailored for efficient multimodal emotion feature extraction, addressing the challenges posed by diverse data types. The proposed framework encompasses a robust storage solution and an innovative SA model, multilevel spatial attention fusion (MLSAF), adept at handling text and visual data concurrently. The data lake architecture comprises five layers, facilitating real-time and offline data collection, storage, processing, standardized interface services, and data mining analysis. The MLSAF model, integrated into the data lake architecture, utilizes a novel approach to SA. It employs a text-guided spatial attention mechanism, fusing textual and visual features to discern subtle emotional interplays. The model’s end-to-end learning approach and attention modules contribute to its efficacy in capturing nuanced sentiment expressions. Empirical evaluations on established multimodal sentiment datasets, MVSA-Single and MVSA-Multi, validate the proposed methodology’s effectiveness. Comparative analyses with state-of-the-art models showcase the superior performance of our approach, with an accuracy improvement of 6% on MVSA-Single and 1.6% on MVSA-Multi. This research significantly contributes to optimizing SA in social media data by offering a versatile and potent framework for data management and analysis. The integration of MLSAF with a scalable data lake architecture presents a strategic innovation poised to navigate the evolving complexities of social media data analytics.
期刊介绍:
IET Software publishes papers on all aspects of the software lifecycle, including design, development, implementation and maintenance. The focus of the journal is on the methods used to develop and maintain software, and their practical application.
Authors are especially encouraged to submit papers on the following topics, although papers on all aspects of software engineering are welcome:
Software and systems requirements engineering
Formal methods, design methods, practice and experience
Software architecture, aspect and object orientation, reuse and re-engineering
Testing, verification and validation techniques
Software dependability and measurement
Human systems engineering and human-computer interaction
Knowledge engineering; expert and knowledge-based systems, intelligent agents
Information systems engineering
Application of software engineering in industry and commerce
Software engineering technology transfer
Management of software development
Theoretical aspects of software development
Machine learning
Big data and big code
Cloud computing
Current Special Issue. Call for papers:
Knowledge Discovery for Software Development - https://digital-library.theiet.org/files/IET_SEN_CFP_KDSD.pdf
Big Data Analytics for Sustainable Software Development - https://digital-library.theiet.org/files/IET_SEN_CFP_BDASSD.pdf