关于推特数据和情绪分析在选举预测中的边界:一篇回顾
Quratulain Alvi1, Syed Farooq Ali1, Sheikh Bilal Ahmed1
1Department of Software Engineering, University of Management and Technology, Lahore, Punjab, Pakistan.
PeerJ. Computer science
|September 14, 2023
概括
这项研究使用推特数据的情绪分析来审查选举预测. 它巩固了研究进展,对方法,引文和技术进行分类,以指导政治学和人工智能的未来探索.
科学领域:
- 计算社会科学 计算社会科学
- 自然语言处理自然语言处理.
- 机器学习 机器学习
背景情况:
- 选举预测利用情绪分析来从在线文本中衡量公众意见.
- 自然语言处理 (NLP) 和机器学习是这个领域的关键技术.
- 在过去的二十年中,选举预测研究取得了重大进展.
研究的目的:
- 检查和巩固选举预测的研究进展,特别是使用Twitter数据.
- 提供当前最先进实践的全面概述.
- 确定未来在该领域进行研究和勘探的潜在途径.
主要方法:
- 系统的文献审查和选举预测研究的分析.
- 基于方法,引用影响和使用的技术的研究分类.
- 专注于利用Twitter数据进行情绪分析和预测的研究.
主要成果:
- 在选举预测中巩固各种方法和技术应用.
- 在选举预测研究的演变中识别趋势和模式.
- 该领域的结构化概述,为了清晰度而分类.
结论:
- 推特数据为选举情绪分析和预测提供了丰富的来源.
- 一种分类方法对于理解该领域的全面进展至关重要.
- 进一步的研究可以建立在已识别的差距和新兴趋势上,以提高预测准确度.
关键词:
分类 分类 分类 分类.深度学习 (Deep Learning) 是一种深度学习.预测选举的预测文学评论 文学评论机器学习 机器学习政策 政策 政策情感分析 情感分析社交媒体 社交媒体社交媒体的分析.他们的推特是Twitter.更多相关视频
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