文本分类算法的旅游景点子类别与修改TF-IDF和Word2Vec的文字分类算法
Lu Xiao1,2,3, Qiaoxing Li1,3, Qian Ma4
1School of Management, Guizhou University, Guiyang, China.
PloS one
|October 18, 2024
概括
这项研究引入了旅游景点描述的新文本分类方法,其性能优于BERT.等现有模型. 这种新的方法增强了文本表示,以提高复杂数据集的准确性和稳定性.
科学领域:
- 自然语言处理自然语言处理.
- 文本挖掘 (Text Mining) 是一个很好的方法.
- 机器学习 机器学习
背景情况:
- 文本分类对于管理大数据至关重要,但其应用于旅游景点等专业领域的应用尚未得到充分探索.
- 现有的方法与专业领域常见的复杂,不平衡的数据集作斗争.
研究的目的:
- 开发和验证旅游景点描述的新型文本表示和分类方法.
- 为了应对分类复杂,不平衡和特定领域文本数据的挑战.
主要方法:
- 使用网络爬虫构建了一个旅游景点描述的语料库.
- 提出了一个混合文本表示,将Word2Vec嵌入式与TF-IDF-CRF-POS权重结合起来.
- 将表示与七个共同分类器 (DT,SVM,LR,NB,MLP,RF,KNN) 集成,用于多类分类.
主要成果:
- 与传统方法和BERT相比,拟议的算法获得了更高的精度 (2.29%) 和F1得分 (宏观F1: 5.55%,微观F1: 2.90%).
- 在不平衡的数据集上表现出卓越的性能,有效地识别罕见的类别.
- 在不同大小的数据集中表现出增强的稳定性.
结论:
- 新的文本表示和分类算法为专业领域的文本提供了卓越的性能和稳定性.
- 该方法很实用,为复杂的中文文本数据集的矢量表达和分类提供了有价值的参考.
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