面向大规模社会媒体的动态舆情内容安全监测关键技术研究

项目名称： 面向大规模社会媒体的动态舆情内容安全监测关键技术研究

项目编号： No.61472258

项目类型： 面上项目

立项/批准年度： 2015

项目学科： 自动化技术、计算机技术

项目作者： 傅向华

作者单位： 深圳大学

项目金额： 82万元

中文摘要： 监测微博、博客等社会媒体所蕴含的舆情内容，关键是及时准确识别其所涉及的话题、情感及演变态势。但社会媒体数据的短文本、不规范、动态更新等特性，导致社会媒体舆情内容监测面临新挑战，存在的问题有：(1)采用词作为特征表示，易产生特征稀疏问题；(2)未对舆情内容进行统一的话题情感混合建模及演变动力学建模；(3)主要以离线方式处理小规模的静态数据。针对上述问题，本项目从四方面开展研究：(1)整合多层语境信息，提出基于深度学习的词向量特征表达学习；(2)基于概率话题模型,提出非参数化的多视角话题情感混合模型；(3)考虑时间维度，建立特征学习与话题情感混合模型的协同更新方式，提出随时演进的话题情感动态演变模型；(4)基于MapReduce等云计算框架，建立能监测大规模社会媒体舆情内容的分布式并行算法。本项目的实施有望提高社会媒体舆情内容安全监测的准确性、自适应性和可扩展性，具有重要的学术价值和应用价值。

中文关键词： 社会媒体；网络信息安全；舆情监测；深度学习；话题情感分析

英文摘要： With the popularity of social media such as micro-blog and blog applications, the general public can write online rivews on the Web very conveniently. The social media include a large amount of public opinion information. To detect and monitor the public opinion information in the social media, the key issues are to identify the topic, sentiment and their dynamic evolutionary trends in these social media timely and accurately. However because of the social media data are short text, irregular expressive, dynamic and large scale, it is a new challenge to detect public opinion in the social media. The main shortages of the existing method are: (1)to represent the features of public opinion information with words, which is easy to cause the problem of feature sparsity; (2) can not represent the topic and sentiment and their dynamic evolutionary of public opinion information with a unified model; (3) to process the public opinion information with a static, off-line and small scale mode. Aiming at above problems, this project research the public opinion information detection from four aspect: the feature representation of social media data, the representation of public opinion, the evolutionary dynamics of public opinion and the large scale data processing. The research content include: (1)integrating muti-layer context information, to propose word vector feature representation based on deep learning models; (2)to propose non-parametrized multi-perspective topic and sentiment model based on probability topic models; (3) to give out the cooperative updating mechanism of feature learning and topic sentiment hybrid model, and to provide dynamic topic sentiment hybrid model with evolutionary ability; (4) to build a series of distributed and parallel algorithms which can detect large scale public opinion information of the social media.The implementation of this project will improve the traditional public opinion security analysis methods, provide theory foundation for the multi-aspect topic and sentiment identification and their evolution analysis, and will improve the adaptive ability, scalability and accuracy of public opinion detection and monitoring task.

英文关键词： social media;network information security;public opinion monitoring;deep learning;topic and sentiment analysis