A Machine Learning Based Strategy for Election Result Prediction

被引：2

作者：

Tsai, Meng-Hsiu ^{[1
]}

Wang, Yingfeng ^{[1
]}

Kwak, Myungjae ^{[1
]}

Rigole, Neil ^{[1
]}

机构：

[1] Middle Georgia State Univ, Dept Informat Technol, Macon, GA 31206 USA

来源：

2019 6TH INTERNATIONAL CONFERENCE ON COMPUTATIONAL SCIENCE AND COMPUTATIONAL INTELLIGENCE (CSCI 2019) | 2019年

基金：

美国国家科学基金会;

关键词：

Twitter; election result prediction; recursive neural tensor network; natural language processing;

D O I：

10.1109/CSCI49370.2019.00263

中图分类号：

TP18 [人工智能理论];

学科分类号：

081104 ; 0812 ; 0835 ; 1405 ;

摘要：

Predicting election results is a hot area in political science. In the last decade, social media has been widely used in political elections. Most approaches can predict the result of a national election. However, it is still challenging to predict the overall results of many local elections. This paper presents a machine learning based strategy to analyze Twitter data for predicting the overall results of many local elections. To verify the effectiveness of this strategy, we apply it for analyzing the Twitter data based on the 2018 midterm election in United States. The results suggest the predicted results are close to the actual election outcome.

引用

页码：1408 / 1410

页数：3

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