Readings in information visualization: using vision to think
Readings in information visualization: using vision to think
Maximum Entropy Markov Models for Information Extraction and Segmentation
ICML '00 Proceedings of the Seventeenth International Conference on Machine Learning
Vizster: Visualizing Online Social Networks
INFOVIS '05 Proceedings of the Proceedings of the 2005 IEEE Symposium on Information Visualization
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IEEE Transactions on Visualization and Computer Graphics
A sentimental education: sentiment analysis using subjectivity summarization based on minimum cuts
ACL '04 Proceedings of the 42nd Annual Meeting on Association for Computational Linguistics
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MUSE: reviving memories using email archives
Proceedings of the 24th annual ACM symposium on User interface software and technology
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WSM '11 Proceedings of the 3rd ACM SIGMM international workshop on Social media
A large-scale sentiment analysis for Yahoo! answers
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Moodcasting: home as shared emotional space
CHI '12 Extended Abstracts on Human Factors in Computing Systems
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Proceedings of the sixth ACM international conference on Web search and data mining
Visualizing ambivalence: showing what mixed feelings look like
CHI '13 Extended Abstracts on Human Factors in Computing Systems
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Spanish knowledge base generation for polarity classification from masses
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Pessimists and optimists: Improving collaborative filtering through sentiment analysis
Expert Systems with Applications: An International Journal
Proceedings of the 17th ACM conference on Computer supported cooperative work & social computing
Proceedings of the 23rd international conference on World wide web
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We present We Feel Fine, an emotional search engine and web-based artwork whose mission is to collect the world's emotions to help people better understand themselves and others. We Feel Fine continuously crawls blogs, microblogs, and social networking sites, extracting sentences that include the words "I feel" or "I am feeling", as well as the gender, age, and location of the people authoring those sentences. The We Feel Fine search interface allows users to search or browse over the resulting sentence-level index, asking questions such as "How did young people in Ohio feel when Obama was elected?" While most research in sentiment analysis focuses on algorithms for extraction and classification of sentiment about given topics, we focus instead on building an interface that provides an engaging means of qualitative exploration of emotional data, and a flexible data collection and serving architecture that enables an ecosystem of data analysis applications. We use our observations on the usage of We Feel Fine to suggest a class of visualizations called Experiential Data Visualization, which focus on immersive item-level interaction with data. We also discuss the implications of such visualizations for crowdsourcing qualitative research in the social sciences.