一种基于自然语言处理的技术,用于对大学英语语体的情感分析
1The School of Humanities and International Education & Exchange, Anhui University of Chinese Medicine, Hefei, China.
PeerJ. Computer science
|June 22, 2023
概括
本研究介绍了使用自然语言处理 (NLP) 分析大学英语公司的改进的术语频率逆文档频率 (TF-IDF) 算法. 增强的TF-IDF模型通过准确识别情绪倾向,显著提高了英语语言学习的效率.
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
- 教育技术的教育技术
背景情况:
- 有效地利用大型大学英语机构对于语言掌握至关重要.
- 从大量文本数据中获取信息存在重大挑战.
- 自然语言处理 (NLP) 为分析语言数据提供了潜在的解决方案.
研究的目的:
- 为大学英语公司开发基于NLP的情感分析模型.
- 提出一个改进的术语频率-反向文档频率 (TF-IDF) 算法,用于增强信息提取.
- 评估模型在确定体内的情感倾向和价值观方面的有效性.
主要方法:
- 实施一种利用NLP技术的情感分析模型.
- 开发了一种增强的TF-IDF算法,其中包括用于情感词汇估值的加权平均方法.
- 整合鼓舞人心的词汇,以确定身体的情感倾向和价值观.
- 在改进的TF-IDF算法中包含信息重量和文字密度处理.
主要成果:
- 拟议的模型在特征词的选择中显示了高的分类准确性.
- 与标准TF-IDF相比,改进的TF-IDF算法表现出更高的操作效率.
- 该模型有效地确定了英语语体的情感倾向和价值.
- 增强的TF-IDF显著提高了大学英语学习的效率.
结论:
- 开发的情绪分析模型,利用改进的TF-IDF算法,是有效的分析大学英语公司.
- 增强的TF-IDF方法在教育应用的准确性和效率方面提供了显著的优势.
- 这种方法提供了一个宝贵的工具,通过数据驱动的见解,优化大学英语学习经验.
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