一种新的混合深度学习ICHOA-CNN-LSTM模型,用于在社交媒体中实现模式丰富和多语言情感识别
1Research Scholar, Faculty of Information and Communication Engineering, UCE-BIT Campus, Tiruchirappalli, Anna University, Chennai, Tamilnadu, India. geethanjaliyokesh18@gmail.com.
Scientific reports
|September 27, 2024
概括
这项研究引入了用于多式联络情绪分析的混合人工智能模型,在COVID-19大流行期间理解公众情绪时达到97.8%的准确性. 先进的框架整合了文本,图像和音频数据,以获得全面的危机洞察力.
科学领域:
- 人工智能的人工智能
- 自然语言处理自然语言处理.
- 计算机视觉 计算机视觉
背景情况:
- 多式联络情绪分析对于了解论至关重要,尤其是在COVID-19大流行等全球危机期间.
- 传统的纯文本情绪分析方法无法捕捉各种数据类型中表达的全部情绪.
- 需要先进的AI技术来处理各种数据源 (文本,图像,音频,视频) 是至关重要的.
研究的目的:
- 开发一种新的混合人工智能模型,用于增强多式联络情绪分析.
- 通过整合各种数据模式,准确地衡量有关COVID-19大流行的公众情绪.
- 通过对流行病话语的全面分析,为公共卫生决策提供可操作的见解.
主要方法:
- 提出了一个混合模型:改进的黑猩猩优化算法-卷积神经网络-长期短期记忆 (IChOA-CNN-LSTM).
- 利用卷积神经网络 (CNN) 来提取图像特征,以及长短期记忆 (LSTM) 网络来处理序列数据.
- 采用了改进的黑猩猩优化算法,以实现跨模式的有效功能融合.
- 整合了GeoCoV19数据集,以进行地理和语言多样化的分析.
主要成果:
- 在多式联络情绪分析中达到97.8%的高准确率.
- 与现有的情绪分析方法相比,表现出优异的表现.
- 成功地整合了各种数据源,包括文本,图像和音频,进行整体分析.
结论:
- IChOA-CNN-LSTM模型为多式联络情绪分析提供了一个强大而准确的框架.
- 这种方法显著提高了在全球卫生危机期间公众情绪的理解.
- 这些发现为公共卫生战略和危机沟通提供了宝贵的见解.
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