Michelle Brachman, Qian Pan, et al.
IUI 2023
We investigate the impact of a discussion snippet's overall sentiment on a user's willingness to read more of a discussion. Using sentiment analysis, we constructed positive, neutral, and negative discussion snippets using the discussion topic and a sample comment from discussions taking place around content on an enterprise social networking site. We computed personalized snippet recommendations for a subset of users and conducted a survey to test how these recommendations were perceived. Our experimental results show that snippets with high sentiments are better discussion "teasers." Copyright © 2010, Association for the Advancement of Artificial Intelligence (www.aaai.org). All rights reserved.
Michelle Brachman, Qian Pan, et al.
IUI 2023
Werner Geyer, Casey Dugan, et al.
CHI 2008
Casey Dugan, Werner Geyer, et al.
CHI 2010
Jürgen Vogel, Werner Geyer, et al.
Computer Supported Cooperative Work