对社交网络情绪分析进行系统审查,并对基于集体的技术进行比较研究
Dimple Tiwari1, Bharti Nagpal2, Bhoopesh Singh Bhati3
1Ambedkar Institute of Advanced Communication Technologies and Research (GGSIPU), Delhi, India.
概括
使用集体学习技术的情感分析 (SA) 对于理解数字文本中的意见至关重要. 基于包装的方法通常优于基于提升的方法来对社交网络情绪进行分类.
科学领域:
- 自然语言处理 (NLP) 是一种自然语言处理.
- 机器学习 机器学习
- 计算语言学 计算语言学
背景情况:
- 情绪分析 (SA) 对于从文本数据中提取意见至关重要.
- 集体学习技术显示出提高SA准确性的巨大潜力.
- 社交媒体平台产生大量的文本数据,需要有效的SA.
研究的目的:
- 提供当前情绪分析方法的系统调查.
- 为了比较包装和增强集体技术的性能,用于社交网络SA.
- 确定挑战,数据集和算法,以推进自动SA.
主要方法:
- 对SA方法的系统文献审查.
- 对包装和提升组合方法的比较分析.
- 在基准数据集上使用准确度,ROC-AUC,F-Score,精度和回忆等指标进行性能评估.
主要成果:
- 基于包装的组合技术在文本分类中比基于提升的技术表现优越.
- 综合评估各种绩效指标提供了对整体方法有效性的见解.
- 该研究确定了与社交网络SA相关的关键挑战和流行的平台.
结论:
- 集体学习,特别是包装,为有效的情绪分析提供了强大的方法.
- 这项研究为未来对社交网络SA的研究提供了有价值的基准信息.
- 这些发现指导了对情绪分类任务的最佳算法的选择.
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