interactions
Technology as Experience
Optimization, maxent models, and conditional estimation without magic
NAACL-Tutorials '03 Proceedings of the 2003 Conference of the North American Chapter of the Association for Computational Linguistics on Human Language Technology: Tutorials - Volume 5
Thumbs up?: sentiment classification using machine learning techniques
EMNLP '02 Proceedings of the ACL-02 conference on Empirical methods in natural language processing - Volume 10
Crowdsourcing user studies with Mechanical Turk
Proceedings of the SIGCHI Conference on Human Factors in Computing Systems
Word usage and posting behaviors: modeling blogs with unobtrusive data collection methods
Proceedings of the SIGCHI Conference on Human Factors in Computing Systems
The interplay of beauty, goodness, and usability in interactive products
Human-Computer Interaction
In CMC we trust: the role of similarity
Proceedings of the SIGCHI Conference on Human Factors in Computing Systems
User experience over time: an initial framework
Proceedings of the SIGCHI Conference on Human Factors in Computing Systems
Let users tell the story: evaluating user experience with experience reports
CHI '10 Extended Abstracts on Human Factors in Computing Systems
Needs, affect, and interactive products - Facets of user experience
Interacting with Computers
Exploring playfulness in user experience of personal mobile products
Proceedings of the 22nd Conference of the Computer-Human Interaction Special Interest Group of Australia on Computer-Human Interaction
Proceedings of the SIGCHI Conference on Human Factors in Computing Systems
Proceedings of the SIGCHI Conference on Human Factors in Computing Systems
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Recent research in user experience (UX) has studied narratives, users' account of their interaction with technology. It has emphasized specific constructs (e.g., affect, needs, hedonics) and their interrelation, but rarely analyzed the content of the narratives. We analyze the content and structure of 691 user-generated narratives on positive and negative experiences with technology. We use a multi-method approach consisting of manual (structural analysis of narratives) as well as of automated content analysis methods (psycholinguistic analysis and machine learning). These analyses show converging evidence that positive narratives predominantly concern social aspects such as family and friends. In addition, technology is positively experienced when it enables users to do things more efficiently or in a new way. In contrast, negative narratives often express anger and frustration due to technological failures. Our multi-method approach illustrates the potential of automated (as opposed to manual) content analysis methods for studying text-based experience reports.