Welcome to ASC 2018

As the rapid growth of user-generated data from social networks, wikis and social tagging systems, it is necessary to understand the high-level semantics and user subjective perceptions from such a large volume of data. Emotions or sentiments are one of the most important aspects as the user-generated data are always with emotional loads of their creators. Along with the development of the computational techniques for sentiment analysis and opinion mining, the increasing psychological and cognitive models/theories are exploited for modeling sentiments and emotions by incorporating with social computing techniques such as social network and personalization, mining user reviews, user profiling in social network and so on. Connecting affective/sentimental models and social computing techniques not only can facilitate the understanding big data in at semantic-levels but also improve the performance of various social computing applications in the big data era. It combines affective/sentimental models with social computing as a promising direction and offers opportunities for developing novel algorithms, methods and tools.


The 2nd International Workshop on Affective and Sentimental Computing (ASC 2018) in conjunction with the 5th IEEE International Conference on Big data and smart Computing (BigComp 2018) will bring together the academia, researchers, and industrial practitioners from computer science, information systems, psychology, behavior science, and organization science discipline, and provide a forum for recent advances in the field of sentiment analysis, affective computing, emotion detections, and opinion mining from the perspectives of various computing techniques.


Topics of interest include, but are not limited to the exploitation of the affective/sentimental models from psychology and cognitive sciences for big data, the identification of emotions/sentiments underlying big data for efficient algorithms for big data management, and the application of affective/sentimental models with social computing techniques in research fields related to (but not limited):

- Sentiment identification & classification
- Emotion identification & classification
- Opinion and sentiment summarization
- Sentiment analysis for social media
- Affective computing for social media
- Time evolving sentiment & emotion analysis
- Knowledge management for affective computing
- Concept-level sentiment analysis
- Social media and social network analysis
- Affective/Sentimental computing for e-learning
- Affective/Sentimental computing for financial data mining
- Affective/Sentimental computing for e-commerce
- Natural language processing techniques for affective computing
- Social ranking
- Social network analysis
- Social tagging analysis
- Affective/Sentimental computing for user modeling
- Data mining algorithms for affective computing
- Big social data analysis
- Information fusion for affective computing


We invite submissions in two categories:

- regular research papers describing completed work or theoretical contributions (up to 8 pages);
- short papers (work-in-progress) (up to 4 pages).


The submission site is


To prepare the manuscript, the templates of IEEE proceedings are adopted in ASC 2018:



PC Members

- Raymong Y. K. Lau, City University of Hong Kong, Hong Kong
- Ke Li, University of Exeter, UK
- Chung Keung Poon, Caritas Institute of Higher Education, Hong Kong
- Jianfeng Si, Institute for Infocomm Research, Singapore
- Xi Tan, Purdue University, USA
- Wenji Ma, Columbia University, USA
- Yunhui Zhuang, City University of Hong Kong, Hong Kong
- Debby Dan Wang, National University of Singapore, Singapore
- Xudong Mao, City University of Hong Kong, Hong Kong
- Di Zou, The Education University of Hong Kong, Hong Kong
- Zhiwen Yu, South China University of Technology, China
- Yi Cai, South China University of Technology, China
- Yanghui Rao, Sun Yat-sen University, China
- Wenjuan Cui, China Academy of Sciences, China
- Raymond Wong, UNWS, Australia
- Tao Wang, Southampton University, UK
- Gualiang Chen, TU Delft, Netherland
- Ling Luo, Sun Yat-Sen University, China


Keynote Speech

Prof. Qing Li

Event Cube: a Conceptual Model for Multi-sourced Event Discovery and Analysis


The publicly available data such as the massive and dynamically updated news and social media data streams (a.k.a. big data) covers the various aspects of social activities, personal views and expressions, which points to the importance of understanding and discovering the knowledge patterns underlying the big data, and the need of developing methodologies and techniques to discover real-world events from such big data, to manage and to analyze the discovered events in an efficient and elegant way. In this talk we introduce techniques of discovering events from the multi-modal big data and building an event cube model to support event queries and analysis, by addressing the tasks of data cleansing, data fusion, event detection and modeling. Preliminary experimental results on some of the tasks will be reported. We further explore and connect the important events discovered in a multimodal collection of inputs from various public sources, uncover their co-occurrence and track down the spatial and temporal dependency to answer the challenging questions of "how" and "why". A novel event cube (EC) model is devised to support various queries and analysis tasks of events; such events include those discovered by techniques of untargeted event detection (UED) and targeted event detection (TED) from multi-sourced data. More specifically, based on essential event elements of 5W1H, the EC model is developed to organize the discovered events from multiple dimensions, and to operate on the events at various levels of granularity, which facilitates analyzing and mining hidden/inherent relationships among the events effectively.


Qing Li is a Professor at the Department of Computer Science, and the Director of the Engineering Research Centre on Multimedia Software at the City University of Hong Kong, where he joined as a faculty member since Sept 1998. He received his B.Eng. from Hunan University (Changsha), and M.Sc. and Ph.D. degrees from the University of Southern California (Los Angeles), all in computer science. His research interests include multi-modal data management, conceptual data modeling, social media and Web services, and e-learning systems. He has authored/co-authored over 400 publications in these areas. He is actively involved in the research community and has served as an associate editor of a number of major technical journals including IEEE Transactions on Knowledge and Data Engineering (TKDE), ACM Transactions on Internet Technology (TOIT), Data and Knowledge Engineering (DKE), World Wide Web (WWW), and Journal of Web Engineering, in addition to being a Conference and Program Chair/Co-Chair of numerous major international conferences. He also sits in the Steering Committees of DASFAA, ER, ICWL, ACM RecSys and IEEE U-MEDIA. Prof. Li is a Fellow of IET (UK), a senior member of IEEE (US) and a distinguished member of CCF (China).



Workshop Programme

January 15, 2018

14:00 - 14:05 Brief Introduction
14:05 - 14:35 Keynote Speech
Title: Event Cube: A Conceptual Model for Multi-sourced Event Discovery and Analysis
Keynote Speaker: Prof. Qing Li (City University of Hong Kong)
14:35 - 14:45 QA
14:45 - 15:05 Paper ID: ASC1 (Regular Paper)
Title: Data Fusion Algorithm Based on Improved D-S Evidence Theory
Authors: Wei Zhang, Xilin Ji, Yang Yang, Zhipeng Gao and Xuesong Qiu
15:05 - 15:15 QA
15:15 - 15:30 Paper ID: ASC3 (Short Paper)
Title: Capture Commonsense Knowledge with Distributed Representation
Authors: Hongming Zhang, Zhaoyu Liu and Yangqiu Song
15:30 - 15:40 QA
15:40 - 16:00 Coffee Break
16:00 - 16:30 Invited Talk
Title: Integrating Feature Weighting in Topic Models
Invited Speaker: Prof. Yi Cai (South China University of Technology)
16:30 - 16:40 QA
16:40 - 16:55 Paper ID: ASC2 (Short Paper)
Title: GAN-based One-class Classification for Personalized Image Retrieval
Authors: Sohyeon Kim, Han-Joon Kim and Jae-Young Chang
16:55 - 17:05 QA
17:05 - 17:25 Paper ID: ASC4 (Regular Paper)
Title: A Clustering based Adaptive Sequence-to-Sequence Model for Dialogue Systems
Authors: Da Ren, Yi Cai, Wai Hong Chan and Zongxi Li
17:25 - 17:35 QA
17:35 - 17:55 Open Discussions
17:55 - 18:00 Workshop Closing