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.