Abstract:

In order to reflect the real public political opinion and improve the accuracy of election prediction, this paper proposes an election forecast model that combines the outcomes of polls and sentiment analysis of netizen. For the poll data, a time-series-based model is used to decrease the bias of polling agencies, and a reverse normalization model is built to infer the political opinion of uncommitted people. For the social network data, a sentiment classification-quantitative model for social network users is used to predict the election result. To improve the prediction accuracy, a fusion-prediction model based on the entropy method is proposed to combine both poll information and sentiment analysis information of network users. Experiments with the ground truth of real historical elections in a certain area show that the proposed model is superior to polls or netizen sentiment analysis according to the accuracy and Mean Relative Error（MRE）.

Key words: social network, sentiment orientation, entropy method, election prediction

摘要：

为接近真实民意，提高选举预测的准确性，提出融合民调与网民情感倾向性的选情预测模型。针对民调数据，利用基于时间序列的数据修正模型减小民调机构的倾向性，并利用反向归一化方法推理民调数据中未表态人群的政治倾向；针对社交网络数据，基于网民情感分类量化模型进行选情预测；提出基于熵值法的选情融合预测模型，利用熵值法融合修正的民调信息与网民情感倾向性信息预测选举结果。以某地区真实历史选举结果为基准的实验表明，所提模型在准确率和平均相对误差指标上优于民调结果或网民情感分析结果。

关键词: 社交网络, 情感倾向性, 熵值法, 选情预测