S
Shuai Wang
Researcher at Chinese Academy of Sciences
Publications - 93
Citations - 2689
Shuai Wang is an academic researcher from Chinese Academy of Sciences. The author has contributed to research in topics: Computer science & Sentiment analysis. The author has an hindex of 18, co-authored 77 publications receiving 1643 citations. Previous affiliations of Shuai Wang include Tsinghua University & University of Illinois at Chicago.
Papers
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Journal ArticleDOI
Deep learning for sentiment analysis: A survey
Lei Zhang,Shuai Wang,Bing Liu +2 more
TL;DR: Deep learning has emerged as a powerful machine learning technique that learns multiple layers of representations or features of the data and produces state-of-the-art prediction results as mentioned in this paper, which is also popularly used in sentiment analysis in recent years.
Posted Content
Deep Learning for Sentiment Analysis : A Survey
Lei Zhang,Shuai Wang,Bing Liu +2 more
TL;DR: An overview of deep learning is given and a comprehensive survey of its current applications in sentiment analysis is provided.
Proceedings ArticleDOI
Target-Sensitive Memory Networks for Aspect Sentiment Classification
TL;DR: The target-sensitive memory networks (TMNs) are proposed, which mean that the sentiment polarity of the (detected) context is dependent on the given target and it cannot be inferred from the context alone.
Proceedings ArticleDOI
Mining Aspect-Specific Opinion using a Holistic Lifelong Topic Model
Shuai Wang,Zhiyuan Chen,Bing Liu +2 more
TL;DR: A holistic fine-grained topic model that can simultaneously model all of above problems under a unified framework is proposed and the idea of lifelong machine learning is incorporated and a more advanced model is proposed, called the LAST (Lifelong Aspect-based Sentiment Topic) model.
Proceedings ArticleDOI
Bimodal Distribution and Co-Bursting in Review Spam Detection
TL;DR: A two-mode Labeled Hidden Markov Model is proposed to model spamming using only individual reviewers' review posting times and a co-bursting network based on co- Bursting relations is proposed, which helps detect groups of spammers more effectively than existing approaches.