树层次的深卷积神经网络,优化了羊群优化算法,用于推特数据的情绪分类
Lakshmanaprakash Sanmugaraja1, Pandiaraj Annamalai2
1Department of Information Technology, Bannari Amman Institute of Technology, Erode, India.
Network (Bristol, England)
|October 21, 2024
概括
这项研究引入了一种新的推特情绪分类模型,提高了准确性和效率. 用羊群优化算法 (SCTD-THDCNN-SFOA) 优化的树层次深卷积神经网络为分析在线数据提供了强大的解决方案.
科学领域:
- 自然语言处理 (NLP) 是一种自然语言处理.
- 机器学习 机器学习
- 计算语言学 计算语言学
背景情况:
- 包括推特在内的在线数据的激增,为准确的情绪分类带来了挑战.
- 获得足够的注释训练数据来进行情绪分析是一个重大障碍.
- 现有的模型往往在分类准确性和计算效率方面都存在问题.
研究的目的:
- 为推特数据开发一个强大的情绪分类模型.
- 为了提高情绪分析的准确性和计算效率.
- 解决处理大量在线文本现有方法的局限性.
主要方法:
- 使用斯坦福情感树木数据集进行培训和评估.
- 使用的预处理技术:标记化,停止词删除,过,删除hashtag和多词分组.
- 使用灰级同发生矩阵窗口自适应算法的特征提取,包括表情符号计数,标点,报纸词,n-grams和POS标签.
- 通过基于透-库尔托斯的方法进行特征选择.
- 实现了一种树层次深度卷积神经网络 (THDCNN),使用羊群优化算法 (SFOA) 进行优化,用于分类.
主要成果:
- 拟议的SCTD-THDCNN-SFOA方法与现有模型相比,实现了更高的性能.
- 在将推特数据分类为积极,消极和中立情绪方面表现出更高的准确性.
- 显著减少了计算时间,表明效率有所提高.
结论:
- SCTD-THDCNN-SFOA框架有效地提高了Twitter数据的情绪分类准确性.
- 该模型为分析大规模在线文本提供了计算效率高的解决方案.
- 这种方法提供了一种强大的方法来克服由于数据量和注释稀缺性而导致的情绪分析挑战.
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