基于深度学习的社交媒体用户的情绪识别
1Institute of Arts and Humanities, Shanghai Jiao Tong University, Shanghai, China.
PeerJ. Computer science
|June 22, 2023
概括
这项研究引入了一个新的用户情绪识别模型,用于社交媒体情绪分析. 该模型提高了微博公众论事件中的情感分类准确性,优于现有的方法.
科学领域:
- 自然语言处理自然语言处理.
- 社交媒体分析
- 计算语言学 计算语言学
背景情况:
- 社交媒体情绪分析面临的挑战是长距离的语义链接和有效的功能词捕获.
- 当前的方法往往严重依赖于手动注释,限制了可扩展性.
研究的目的:
- 开发一种用户情绪识别模型,用于分析微博上公众论事件.
- 为了提高社交媒体情感分类的准确性. 文本.
主要方法:
- 使用线性差别分析 (LDA),情感字典,以及用于特征词提取的手动注释.
- 使用Word2vec进行词向量转换和双向长短期记忆 (BiLSTM) 与卷积神经网络 (CNN) 进行语义数据收集和特征提取.
- 专注于三个核心情绪:快乐,愤怒和悲伤.
主要成果:
- 实现了机器学习模型的平均F1分数增长3.66%,深度学习模型的平均F1分数增长1.84%.
- 与现有方法相比,拟议的模型在识别用户情绪方面表现出优异的性能.
结论:
- 开发的模型有效地解决了当前社交媒体情绪分析的局限性.
- 这项研究为微博公众论事件情绪分析提供了更强大的方法.
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