一个Al-BERT-Bi-GRU-LDA算法用于对Bilibili评论的负面情绪分析
1School of Education, Guizhou Normal University, Guiyang, China.
PeerJ. Computer science
|June 10, 2024
概括
这项研究引入了一个AI-BERT+BiGRU+LDA算法来分析在线数学课程的学生反. 混合模型有效地提取情绪并确定关键问题,提高教育质量.
科学领域:
- 教育技术的教育技术
- 教育中的人工智能
- 自然语言处理自然语言处理.
背景情况:
- 越来越多地采用在线自学和终身教育计划.
- 学生的评论为学习经验,意见和投诉提供了宝贵的见解.
- 需要有效的情绪分析工具来进行教育反.
研究的目的:
- 开发和评估一种混合算法,用于对学生在线学习反的情感分析.
- 从高中数学课程上的学生评论中识别关键主题和问题.
- 根据学生的反,提高课程和教学质量.
主要方法:
- 利用Bilibili平台上的10个高中数学课程的学生评论数据集.
- 构建了一个混合算法:人工智能-双向编码器从变压器 (BERT) +双向门式循环单元 (BiGRU) 和线性差异分析 (LDA) 的表示.
- 在教育审查数据集上使用替代算法进行比较分析.
主要成果:
- 与其他算法相比,拟议的混合算法表现出更高的精度和更低的损失率.
- 在测试过程中,该算法始终保持低损失比率.
- 对负面评论的话题挖掘揭示了杂乱内容,课程复杂性和视频质量的问题.
结论:
- AI-BERT+BiGRU+LDA混合算法在在线教育中的情绪分析中是有效的.
- 学生反突出了课程内容,交付和视频制作方面的改进领域.
- 这些发现为解决学生关切和加强教育提供提供提供了基础.
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