.png){kind=icon}
返回
Collaborative community-specific microblog sentiment analysis via multi-task learning
.png){kind=icon}
PRE
AI
DOI：10.1016/j.eswa.2020.114322.png)
.png){kind=icon}
原文求助.png){kind=icon}
分享.png){kind=icon}
收藏
摘要
En 中文
Microblog sentiment analysis has become a hot research area due to its wide applications. There are some methods utilizing social context, but they only built a global sentiment analysis model, failing to extract personalized expressions. Some personalized methods have been proposed to deal with this problem, but they suffer from data sparseness and inefficiency. Based on personalized sentiment analysis methods, we exploit social context information and capture users' variable and distinctive expressions at a community level to handle these problems. In particular, we propose a collaborative microblog sentiment analysis approach. In our approach, two classifiers are constructed. One is the global microblog sentiment analysis model which can exploit the sentiment shared by all users. One is the community-specific microblog sentiment analysis model which can extract sentiment influenced by user personalities. In addition, we extract community similarity knowledge and employ it to improve the learning process of the community-specific sentiment model. Moreover, we incorporate social contexts into this model as regularization to encourage the sharing sentiment between connected microblogs. An accelerated algorithm is introduced to solve our model. Experiments on two real datasets show that our model can advance the performance of microblog sentiment classification effectively and outperform state-of-art methods significantly.
Keyword:
Sentiment analysis
Microblogging
Multi-task learning
Social context
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:
2.9W
被引数:
10.2W
机构
引用论文
Update on the Clinical Diagnosis, Management, and Pathogenesis of Pulmonary Alveolar Proteinosis (Phospholipidosis).png)
Chest
ChestIF0
PREAI
分享收藏Twitter Sentiment Analysis for Large-Scale Data: An Unsupervised Approach大规模数据的Twitter情感分析: 一种无监督的方法
PREAI
分享收藏Using Seawater Reverse Osmosis (SWRO) desalting system for less environmental impacts in Qatar在卡塔尔使用海水反渗透 (SWRO) 脱盐系统减少环境影响Desalination
DesalinationIF0
PREAI
分享收藏
查看更多内容