基于自适应特征词的微博噪音过滤方法
Microblog Noise Filtering Method Based on Self-adaptive Characteristics
张晓瑜 1高扬 2苗星星 1祝永霞3
作者信息
- 1. 中国人民解放军32317部队 乌鲁木齐 830000
- 2. 中国人民解放军32319部队 乌鲁木齐 830000
- 3. 陆军边海防学院 乌鲁木齐 830000
- 折叠
摘要
微博噪音过滤具有去除垃圾样本、降低数据规模等作用.利用聚类算法产生噪音种子词,在未标注数据上采用FP-Growth算法对种子词扩展,生成噪音特征词词典,结合用户和内容特征,引入支持向量机模型用于噪音微博过滤.实验结果正确率为84%,召回率为79%,F1值为81%,证明该模型产生的噪音特征词有助于提升微博过滤的效果.
Abstract
Microblog noise filtering can remove garbage samples and reduce data scale.The noise seed words are generated by the clustering algorithm.FP-Growth algorithm is used to expand the seed words on unlabeled data to generate a noise feature word dictionary.Combining user and content characteristics,the support vector machine model is introduced to filter noise microblogs.The experimental results shows that the precision is 84%,the recall is 79%,the F1 value is 81%,which proves that the noise char-acteristics generated by the model can help to improve the filtering effect of microblog.
关键词
微博/自适应/噪音特征词/支持向量机Key words
microblog/self-adaptive/noise characteristics/SVM引用本文复制引用
出版年
2024
国家科技期刊平台
NSTL
万方数据