组装堆叠模型,以更好地识别来自IMDB评论的情绪
Komal Azim1, Alishba Tahir1, Mobeen Shahroz1
1Department of Artificial Intelligence, The Islamia University of Bahawalpur, Bahawalpur, 63100, Pakistan.
Scientific reports
|April 18, 2025
概括
这项研究引入了乌尔都语情绪分析的整体模型,在电影评论和推特上获得了92.77%的准确性. 结合多个算法和SMOTE的RRLS模型显著改善了乌尔都语社交媒体内容的情绪检测.
科学领域:
- 自然语言处理自然语言处理.
- 计算语言学 计算语言学
- 人工智能的人工智能
背景情况:
- 社交媒体平台有助于广泛分享意见和情绪.
- 情绪分析是一个关键的研究领域,但乌尔都语情绪分析仍然不发达.
- 乌尔都语说者广泛使用社交媒体,强调需要有效的乌尔都语情绪分析工具.
研究的目的:
- 开发和评估一个集体模型,以准确地分析乌尔都语的情感.
- 在情感分析中解决乌尔都语字符编码和规范化的挑战.
- 为了比较拟议的集合模型与单个机器学习算法的性能.
主要方法:
- 开发了一个名为RRLS的集合模型,集成了随机森林,循环神经网络,后勤回归 (LR) 和支持向量机器 (SVM).
- 乌尔都语黑客库被用于数据预处理,包括字符规范化和标点处理.
- 为了提高模型性能,使用了合成少数人过量采样技术 (SMOTE).
主要成果:
- 像SVM和LR这样的单个模型达到87%的准确性,而LSTM和BiLSTM达到84%.
- 拟议的RRLS组合模型表现出卓越的性能,达到90%的准确性.
- 应用SMOTE进一步提高了整体模型的准确性,达到92.77%.
结论:
- 该RRLS组合模型显著优于乌尔都语情绪分析的现有方法.
- 数据预处理技术,包括字符规范化和过量采样,对于提高乌尔都语情绪分析准确度至关重要.
- 这项研究为分析社交媒体平台上乌尔都语表达的情绪提供了强有力的解决方案.
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