基于使用分类技术的情绪分析,在社交媒体上检测假新闻
Sarita V Balshetwar1,2, Abilash Rs3, Dani Jermisha R4
1YSPM'S, YTC, Faculty of Engineering, Satara, Maharashtra India.
概括
这项研究引入了一种新的假新闻检测方法,使用情绪分析和先进的特征提取技术. 提出的方法达到99.8%的准确性,超过现有的方法在线识别虚假信息.
科学领域:
- 自然语言处理自然语言处理.
- 机器学习 机器学习
- 计算社会科学 计算社会科学
背景情况:
- 在社交媒体上假新闻的蔓延带来了重大的社会挑战.
- 准确的假新闻检测对于保持信息完整性至关重要.
- 现有的方法经常与在线内容和缺失数据的细微差别作斗争.
研究的目的:
- 开发一个准确和高效的假新闻检测系统.
- 将情绪分析纳入作为改进检测的关键功能.
- 解决社交媒体数据集中缺少数据的挑战.
主要方法:
- 使用基于词典的评分算法来识别关键意见特征的情感分析.
- 多重推算链方程 (MICE) 用于处理多变量缺失数据.
- 术语频率-反向文档频率 (TF-IDF) 用于特征提取.
- 使用纯粹贝叶斯,被动攻击和深度神经网络 (DNN) 模型进行分类.
主要成果:
- 提出的方法在检测假新闻方面实现了99.8%的准确率.
- 该系统有效地评估了各种真实程度的陈述 (几乎真实,半真实,真实,大部分真实,错误).
- 与现有的假新闻检测方法相比,这种方法表现出了优越的性能.
结论:
- 整合情绪分析显著提高了假新闻检测准确度.
- 拟议的方法提供了一个强大的解决方案,用于识别社交媒体上的错误信息.
- 这项研究为打击虚假新闻传播提供了有价值的工具.
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