Abstract：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.
[1] 杨阳,林鸿飞,杨亮,等.大数据时代的计算政治学研究.中文信息学报, 2017, 31(3): 9-16. (YANG Y, LIN H F, YANG L, et al. Computational Political Science in the Era of Big Data. Journal of Chinese Information Processing, 2017, 31(3): 9-16.) [2] POOLE K T, ROSENTHAL H.A Spatial Model for Legislative Roll Call Analysis. American Journal of Political Science, 1985, 29(2): 357-384. [3] SIMON J.Multidimensional Analysis of Roll Call Data via Bayesian Simulation: Identification, Estimation, Inference, and Model Che-cking. Political Analysis, 2001, 9(3): 227-241. [4] CLINTON J, JACKMAN S, RIVERS D.The Statistical Analysis of Roll Call Data. American Political Science Review, 2004, 98(2): 355-370. [5] GERRISH S M, BLEI D M.Predicting Legislative Roll Calls from Text//Proc of the 28th International Conference on Machine Lear-ning. New York, USA: ACM, 2011: 489-496. [6] YANO T, SMITH N A, WILKERSON J D.Textual Predictors of Bill Survival in Congressional Committees//Proc of the Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies. Stroudsburg, USA: ACL, 2012: 793-802. [7] NAY J J.Predicting and Understanding Law-Making with Word Vectors and an Ensemble Model. PLoS One, 2017, 12(5). DOI: 10.1371/journal.pone.0176999. [8] YANG Y Q, LIN X Q, LIN G, et al. Joint Representation Learning of Legislator and Legislation for Roll Call Prediction//Proc of the 29th International Joint Conference on Artificial Intelligence. San Francisco, USA: IJCAI, 2020: 1424-1430. [9] KIPF T N, WELLING M.Semi-Supervised Classification with Graph Convolutional Networks[C/OL]. [2021-07-28].https://arxiv.org/pdf/1609.02907v4.pdf. [10] BASTINGS J, TITOV I, AZIZ W, et al. Graph Convolutional Encoders for Syntax-Aware Neural Machine Translation//Proc of the Conference on Empirical Methods in Natural Language Processing. Stroudsburg, USA: ACL, 2017: 1957-1967. [11] NGUYEN T H, GRISHMAN R.Graph Convolutional Networks with Argument-Aware Pooling for Event Detection. Proceeding of the AAAI Conference on Artificial Intelligence, 2018, 32(1): 5900-5907. [12] YAO L, MAO C S, LUO Y.Graph Convolutional Networks for Text Classification. Proceeding of the AAAI Conference on Artificial Intelligence, 2019, 33(1): 7370-7377. [13] ZHANG J C, HE Q, ZHANG Y.Syntax Grounded Graph Convolutional Network for Joint Entity and Event Extraction. Neurocompu-ting, 2021, 422: 118-128. [14] HAMMOND D K, VANDERGHEYNST P, GRIBONVAL R.Wave-lets on Graphs via Spectral Graph Theory. Applied and Computational Harmonic Analysis, 2011, 30(2): 129-150. [15] DEVLIN J, CHANG M W, LEE K, et al. Bert: Pre-training of Deep Bidirectional Transformers for Language Understanding//Proc of the Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies. Stroudsburg, USA: ACL, 2019: 4171-4186. [16] PEROZZI B, AL-RFOU R, SKIENA S.DeepWalk: Online Lear-ning of Social Representations//Proc of the 20th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining. New York, USA: ACM, 2014: 701-710. [17] PENNINGTON J, SOCHER R, MANNING C D.GloVe: Global Vectors for Word Representation//Proc of the Conference on Empirical Methods in Natural Language Processing. Stroudsburg, USA: ACL, 2014: 1532-1543. [18] JOULIN A, GRAVE E, BOJANOWSKI P, et al. FastText.zip: Compressing Text Classification Models[C/OL]. [2021-07-28]. https://arxiv.org/pdf/1612.03651.pdf.
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