.png){kind=icon}
返回
CDS: Collaborative distant supervision for Twitter account classification
.png){kind=icon}
PRE
AI
DOI：10.1016/j.eswa.2017.03.075.png)
.png){kind=icon}
原文求助.png){kind=icon}
分享.png){kind=icon}
收藏
摘要
En 中文
Individuals use Twitter for personal communication, whereas businesses, politicians and celebrities use Twitter for branding purposes. Distinguishing Personal from Branding Twitter accounts is important for Twitter analytics. Existing studies of Twitter account classification apply classical supervised learning, which requires intensive manual annotation for training. In this paper, we propose CDS (Collaborative Distant Supervision), a novel learning scheme for Twitter account classification that does not require intensive manual labelling. Twitter accounts are automatically labelled using heuristics for distant supervision learning. To achieve effective learning from heuristic labels, active learning is applied to identify and correct false positive labels, and semi-supervised learning is applied to further use false negatives missed by labelling heuristics for learning. Extensive experiments on Twitter data showed that CDS achieved high classification accuracy. (C) 2017 Elsevier Ltd. All rights reserved.
Keyword:
Twitter
Classification
Distant supervision
Active learning
Semi-supervised learning
AI总结
对已上传原文的论文进行重点信息的提取，主要内容包括：简要概述、研究摘要、背景介绍、关键亮点、图文解析、展望与总结。
期刊
IF:
7.5
论文数:
3.0W
被引数:
10.2W
机构
引用论文
Comparison of the effects of intravenous methylprednisolone pulse versus oral prednisolone therapies on the first attack of minimal‐change nephrotic syndrome in adults.png)
Nephrology
NephrologyIF0
PREAI
分享收藏
查看更多内容