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.
Fund:Support by National Natural Science Foundation of China(No.61632011,62076046,62076051)
Corresponding Authors:
LIN Hongfei, Ph.D., professor. His research interests include sentiment analysis and opinion mining, information retrieval and recommendation, and knowledge mining.
About author:: ZHANG Xinyun, master student. His research interests include artificial intelligence and natural language processing.ZHANG Shaowu, Ph.D., professor. His research interests include text mining, natural language processing and affective computing.REN Lu, Ph.D. candidate. Her research interests include emotional analysis and natural language processing.YANG Liang, Ph.D., lecturer. His research interests include natural language processing and sentiment analysis.XU Bo, Ph.D., associate professor. His research interests include natural language processing and information retrieval.ZHANG Yijia, Ph.D., professor. His research interests include natural language processing and text mining.
ZHANG Xinyun,ZHANG Shaowu,REN Lu等. Voting Prediction Model Based on Voter Influence Factor[J]. Pattern Recognition and Artificial Intelligence, 2022, 35(2): 166-174.
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