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论文编号：	15569
作者编号：	2320234210
上传时间：	2025/12/6 9:03:58
中文题目：	基于多模态数据融合的舆情风险预警研究
英文题目：	Research on Public Opinion Risk Early Warning under the Framework of Multimodal Data Fusion
指导老师：	王芳
中文关键字：	多模态数据；社会风险；舆情风险；风险预警
英文关键字：	Multimodal Data;Social Risks;Public Opinion Risk;Risk Early Warning
中文摘要：	针对社会风险中的舆情风险，本研究深入探讨了基于多模态数据融合的预警方法，旨在构建一个高效、精准的舆情风险预警系统。研究广泛采集了社交媒体、传感器网络、政府公开数据库、新闻报道及学术文献等多源数据，通过网络爬虫技术和传感器设备确保数据的丰富性与全面性。在数据预处理阶段，采用自然语言处理、图像去噪、音频滤波等技术对文本、图像、音频及视频等不同类型的数据进行精细化清洗与标注，显著提升数据质量。 研究运用特征提取与转换策略，结合词嵌入、卷积神经网络及短时傅里叶变换等技术，精准提取各模态数据中的代表性特征。通过特征级与决策级融合，并引入注意力机制，实现多模态数据的有效整合，充分挖掘数据间的互补信息，为舆情风险预警提供全面、准确的数据支持。 基于多模态数据融合技术，本研究开发了一个基于Transformer架构的舆情风险预警模型。该模型融合了特征提取、融合与分类模块，充分展现了Transformer在语言理解与全局建模方面的卓越性能。结合多模态数据特征，模型能够更精准地识别与预测舆情风险。在模型训练与优化过程中，采用随机梯度下降等优化算法，并辅以L1、 L2正则化及Dropout技术，有效防止模型过拟合，显著增强模型的稳定性与泛化能力。 通过实际案例分析，验证了该多模态数据融合的舆情风险预警模型具有高度的有效性与实用性。研究结果表明，该模型能够显著提升舆情风险预警的准确性与及时性，为政府机关、社会组织和企业等提供决策依据，帮助他们提前制定应对策略，降低潜在损失。本研究不仅丰富了舆情风险预警的研究现状，还促进了相关学科的交叉融合与发展，为社会的稳定与持续发展提供了支持。
英文摘要：	In response to public opinion risk among social risks, this study delves into the early warning methodology based on multimodal data fusion, aiming to construct an efficient and precise public opinion risk early warning system. The research extensively collects multisource data from social media, sensor networks, government open databases, news reports, and academic literature, ensuring data richness and comprehensiveness through web crawling technology and sensor devices. During the data preprocessing phase, techniques such as natural language processing, image denoising, and audio filtering are employed to meticulously clean and annotate various types of data, including text, images, audio, and video, significantly enhancing data quality. The study innovatively applies feature extraction and transformation strategies, integrating technologies like word embedding, convolutional neural networks, and short-time Fourier transforms to accurately extract representative features from each modality of data. Through feature-level and decision-level fusion, coupled with the incorporation of an attention mechanism, effective integration of multimodal data is achieved, fully exploiting the complementary information among data sources and providing comprehensive and accurate data support for public opinion risk early warning. Based on multimodal data fusion technology, this research has developed a public opinion risk early warning model grounded in the Transformer architecture. This model seamlessly integrates feature extraction, fusion, and classification modules, fully demonstrating the exceptional performance of Transformer in language understanding and global modeling. By leveraging multimodal data features, the model can more precisely identify and predict public opinion risk. During the model training and optimization process, optimization algorithms such as stochastic gradient descent are employed, supplemented by L1 and L2 regularization and Dropout techniques, effectively preventing model overfitting and significantly enhancing model stability and generalization capabilities. Through practical case analysis, the effectiveness and practicality of this multimodal data fusion-based public opinion risk early warning model have been validated. The research findings indicate that the model can significantly improve the accuracy and timeliness of public opinion risk early warning, providing decision-making bases for government agencies, social organizations, and enterprises, aiding them in formulating proactive response strategies and reducing potential losses. This study not only enriches the theoretical framework of public opinion risk early warning but also promotes interdisciplinary integration and development, offering robust support for social stability and sustainable development.
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