Knowledge Commons of Institute of Automation,CAS
Web-derived Emotional Word Detection in social media using Latent Semantic information | |
Chiyu Cai1,2; Linjing Li1; Daniel Zeng1,3 | |
2017-07 | |
会议名称 | 2017 IEEE International Conference on Intelligence and Security Informatics |
会议日期 | July 22-24, 2017 |
会议地点 | Beijing |
摘要 | Public sentiment permeated through social media is usually regarded as an important measure for public opinion monitoring, policy making, and so forth. However, the deluge of user-generated content in web, especially in social platform, causes great challenge to public sentiment analysis tasks. Therefore, Web-derived Emotional Word Detection (WEWD) is proposed as a fundamental tool aims to alleviate this problem. Most previous works on WEWD focus on rules, syntax, and sentence structures, a few utilize semantic information which has the potential to further increase the accuracy and efficiency of WEWD. In this paper, we propose a Global-Local Latent Semantic (GLLS) framework for WEWD to make a full use of latent semantic information with the help of multiple sense word embedding technology. We devise two computational WEWD models, called Ensemble GLLS (EGLLS) and Deep GLLS (DGLLS). EGLLS exploits an ensemble learning way to fuse the global and local latent semantics while DGLLS takes advantage of deep neural network. We also design an old-new corpus enrich technique to help increase the effectiveness of the overall training and detecting process. To the best of our knowledge, this is the first work which applies multiple sense word embedding and deep neural network in WEWD related tasks. Experiments on real datasets demonstrate the effectiveness of the proposed idea and methods. |
收录类别 | EI |
文献类型 | 会议论文 |
条目标识符 | http://ir.ia.ac.cn/handle/173211/19863 |
专题 | 多模态人工智能系统全国重点实验室_互联网大数据与信息安全 |
作者单位 | 1.The State Key Laboratory of Management and Control for Complex Systems, Institute of Automation, Chinese Academy of Sciences 2.University of Chinese Academy of Sciences 3.University of Arizona |
第一作者单位 | 中国科学院自动化研究所 |
推荐引用方式 GB/T 7714 | Chiyu Cai,Linjing Li,Daniel Zeng. Web-derived Emotional Word Detection in social media using Latent Semantic information[C],2017. |
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文件名称/大小 | 文献类型 | 版本类型 | 开放类型 | 使用许可 | ||
Web-derived Emotiona(328KB) | 会议论文 | 开放获取 | CC BY-NC-SA | 浏览 下载 |
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