Modeling the Shape of the Scene: A Holistic Representation of the Spatial Envelope
International Journal of Computer Vision
Histograms of Oriented Gradients for Human Detection
CVPR '05 Proceedings of the 2005 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR'05) - Volume 1 - Volume 01
ACM SIGGRAPH 2007 papers
Analyzing and predicting sentiment of images on the social web
Proceedings of the international conference on Multimedia
Effective sentiment stream analysis with self-augmenting training and demand-driven projection
Proceedings of the 34th international ACM SIGIR conference on Research and development in Information Retrieval
From bias to opinion: a transfer-learning approach to real-time sentiment analysis
Proceedings of the 17th ACM SIGKDD international conference on Knowledge discovery and data mining
User-level sentiment analysis incorporating social networks
Proceedings of the 17th ACM SIGKDD international conference on Knowledge discovery and data mining
Twitter polarity classification with label propagation over lexical links and the follower graph
EMNLP '11 Proceedings of the First Workshop on Unsupervised Learning in NLP
Exploiting social relations for sentiment analysis in microblogging
Proceedings of the sixth ACM international conference on Web search and data mining
Major life changes and behavioral markers in social media: case of childbirth
Proceedings of the 2013 conference on Computer supported cooperative work
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Online social networks have attracted attention of people from both the academia and real world. In particular, the rich multimedia information accumulated in recent years provides an easy and convenient way for more active communication between people. This offers an opportunity to research people's behaviors and activities based on those multimedia content, which can be considered as social imagematics. One emerging area is driven by the fact that these massive multimedia data contain people's daily sentiments and opinions. However, existing sentiment analysis typically only pays attention to the textual information regardless of the visual content, which may be more informative in expressing people's sentiments and opinions. In this paper, we attempt to analyze the online sentiment changes of social media users using both the textual and visual content. In particular, we analyze the sentiment changes of Twitter users using both textual and visual features. An empirical study of real Twitter data sets indicates that the sentiments expressed in textual content and visual content are correlated. The preliminary results in this paper give insight into the important role of visual content in online social media.