对形容词和副词类型的评估,以有效地对Twitter情绪进行分类
Syed Fahad Ali1, Nayyer Masood1
1Capital University of Science & Technology, Islamabad, Pakistan.
PloS one
|May 1, 2024
概括
这项研究通过探索各种形容词和副词类型来增强情绪分析,提高分类准确率高达83%,并优于现有的模型,用于更快,更准确的推特分析.
科学领域:
- 自然语言处理自然语言处理.
- 机器学习 机器学习
- 计算语言学 计算语言学
背景情况:
- 推特是用户表达情绪的主要平台.
- 情绪分析对推文进行分类,以了解用户的意见.
- 特征选择,特别是语音部分 (POS),对于情感分类至关重要.
研究的目的:
- 调查各种形容词和副词类型对情感分类准确性的影响.
- 分析组合不同形容词和副词类型的有效性.
- 将拟议的方法与基准数据集和现有模型进行比较.
主要方法:
- 利用人类注释的推特数据集进行情绪分析.
- 检查了特定类型的形容词 (例如,上级词) 和副词 (例如,比较词).
- 评估了形容词和副词类型的组合,以改进分类.
主要成果:
- 使用一般的超级形容词达到高达83%的分类准确度.
- 与比较的一般形容词显著提高了准确性.
- 一般形容词和副词的组合也大大改善了结果.
结论:
- 未经探索的形容词和副词类型与最先进的概率模型相比,提供更高的准确性.
- 拟议的模型减少了对基于词典的词典的依赖,加速了分析.
- 这项研究为情绪分析和高效的数据处理提供了宝贵的见解.
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