A Machine Learning Based Strategy for Election Result Prediction

被引：3
|
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
收藏
页码：1408 / 1410
页数：3
相关论文
共 50 条
  • [41] Machine learning based cardiovascular disease prediction
    Chinnasamy, P.
    Kumar, S. Arun
    Navya, V.
    Priya, K. Lakshmi
    Boddu, Siva Sruthi
    MATERIALS TODAY-PROCEEDINGS, 2022, 64 : 459 - 463
  • [42] Machine Learning Based LncRNA Function Prediction
    Liu, Yu
    Zheng, Xianghan
    Rang, Chunming
    2017 INTERNATIONAL CONFERENCE ON GREEN INFORMATICS (ICGI), 2017, : 67 - 70
  • [43] Prediction of Crops Based on a Machine Learning Algorithm
    SRM Institute of Science and Technology, Department of Networking and Communications, Tamil Nadu, Chennai
    603203, India
    Int. Conf. Comput. Commun. Informatics, ICCCI, 2023,
  • [44] Prediction of Customer Propensity Based on Machine Learning
    Zhang, Yue
    2021 ASIA-PACIFIC CONFERENCE ON COMMUNICATIONS TECHNOLOGY AND COMPUTER SCIENCE (ACCTCS 2021), 2021, : 5 - 9
  • [45] Interpretable prediction of remanence in sintered NdFeB through machine learning strategy
    Wang, Zihao
    Chang, Shuai
    Bao, Xiaoqian
    Yu, Haijun
    Guan, Shengen
    Zhu, Kunyuan
    Zheng, Yang
    Li, Jiheng
    Gao, Xuexu
    JOURNAL OF ALLOYS AND COMPOUNDS, 2024, 1008
  • [46] Machine Learning Strategy for Wall Heat Flux Prediction in Aerodynamic Heating
    Dai, Gang
    Zhao, Wenwen
    Yao, Shaobo
    Chen, Weifang
    JOURNAL OF THERMOPHYSICS AND HEAT TRANSFER, 2023, 37 (02) : 424 - 434
  • [47] Peeking strategy for online news diffusion prediction via machine learning
    Zhang, Yaotian
    Feng, Mingming
    Shang, Ke-ke
    Ran, Yijun
    Wang, Cheng-Jun
    PHYSICA A-STATISTICAL MECHANICS AND ITS APPLICATIONS, 2022, 598
  • [48] A hybrid machine learning strategy for pitting probability prediction of stainless steels
    Qu, Zhihao
    Cheng, Kexin
    Jiang, Xue
    Wang, Zhu
    Su, Yanjing
    Zhang, Lei
    MATERIALS TODAY COMMUNICATIONS, 2024, 40
  • [49] Sampling Strategy Analysis of Machine Learning Models for Energy Consumption Prediction
    Wu, Zeqing
    Chu, Weishen
    2021 THE 9TH IEEE INTERNATIONAL CONFERENCE ON SMART ENERGY GRID ENGINEERING (SEGE 2021), 2021, : 77 - 81
  • [50] Machine Learning Strategy for Soil Nutrients Prediction Using Spectroscopic Method
    Ml, Janez Trontelj
    Chambers, Olga
    SENSORS, 2021, 21 (12)
12345