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
专题复杂系统管理与控制国家重点实验室_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
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Chiyu Cai,Linjing Li,Daniel Zeng. Web-derived Emotional Word Detection in social media using Latent Semantic information[C],2017.
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