基于投票人影响因子的投票预测模型  

Voting Prediction Model Based on Voter Influence Factor

作　　者：张新昀 张绍武 任璐 杨亮 徐博 张益嘉 林鸿飞 ZHANG Xinyun;ZHANG Shaowu;REN Lu;YANG Liang;XU Bo;ZHANG Yijia;LIN Hongfei(Information Retrieval Laboratory,Dalian University of Technology,Dalian 116023)

机构地区：[1]大连理工大学信息检索研究室,大连116023

出　　处：《模式识别与人工智能》2022年第2期166-174,共9页Pattern Recognition and Artificial Intelligence

基　　金：国家自然科学基金项目(No.61632011,62076046,62076051)资助。

摘　　要：投票预测是计算政治学的应用之一,目前绝大多数预测模型都忽视投票过程中投票人之间的相互影响.针对此问题,文中提出基于投票人影响因子的投票预测模型.首先,提出投票人影响因子,用于刻画某位投票人在投票过程中对于其他投票人投票选择的影响,同时结合预训练模型提取的投票人特征,形成影响因子图,再输入图卷积神经网络中,学习投票人的相互影响,在一定程度上模拟真实的投票博弈过程.然后,考虑到法案文本中上下文信息的关联性,利用BiLSTM(Bi-directional Long Short-Term Memory)获得法案特征向量.鉴于法案文本的规范性导致的行文近似、用词重复,使用引入TF-IDF(Term-Frequency-Inverse Document Frequency)因子的TextRank,得到法案的关键词.在国外议会网站数据集上的实验表明文中模型性能较优,消融实验也验证每个模块对文中模型的性能均有一定程度的提升.Voting prediction is one of the applications of computational politics.However,the interaction between voters in the voting is ignored by most of the prediction models.To solve this problem,a voting prediction model based on voter influence factor is proposed in this paper.Firstly,the voter influence factor is proposed to describe the influence of a voter on the voting choices of other voters in the voting process.A factor graph is generated by combining the voter influence factor and the voter characteristics extracted by the pre-training model.Then,the factor graph is introduced into graph convolution neural network to learn the interaction of voters and to simulate the real voting game to a certain extent.Considering the relevance of context information in the text of the bill,bi-directional long short-term memory is utilized to obtain the feature vector of bill.In view of similar writing and repetition of words caused by standardization of the bill text,the key words of the bill are obtained by TextRank with term-frequency-inverse document frequency factor.Finally,experiments on the foreign congress website dataset show that the performance of the proposed model is superior.The ablation experiments verify that each module improves the performance of the model to a certain extent.

关 键 词：计算政治学 投票预测 图卷积神经网络 影响因子 深度学习 

分 类 号：TP391.1[自动化与计算机技术—计算机应用技术] TP18[自动化与计算机技术—计算机科学与技术]