Name
Abstract | ||
|---|---|---|
Communication is social by nature, and reveals psycho-social dimensions about an actor’s perceptions of themself and others. While grammar and spell-check can help polish the presentation of communication, it does not reflect the way that a message will be received in a particular social space. A means to analyze the communication for actor beliefs can help the author and others understand the underlying social climate and message that is being transmitted. NASA has identified the need to monitor individual behavioral health and team dynamics as crucial to ensuring high performance and mission success. We describe an application that integrates theories from sociolinguistics with natural language processing techniques to successfully detect individual moods, attitudes, and team dynamics relevant to long duration exploration class missions. The methods were used to analyze data gathered from human subject experiments at three diverse analog studies, with results showing high correlation with subject self-reports and third party observations. We discuss preliminary results and implications for the tool’s potential wide-spread use. |
Year | Venue | Field |
|---|---|---|
2015 | AAAI Spring Symposia | Sociolinguistics,Computer science,Psychosocial,Social space,Grammar,Third party,Artificial intelligence,Latent semantic analysis,Perception,Machine learning |
DocType | Citations | PageRank |
Conference | 0 | 0.34 |
References | Authors | |
0 | 5 | |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Peggy Wu | 1 | 22 | 7.10 |
| Christopher A. Miller | 2 | 334 | 46.70 |
| Tammy Ott | 3 | 11 | 4.42 |
| Sonja Schmer-Galunder | 4 | 4 | 2.99 |
| Jeffrey Rye | 5 | 12 | 5.72 |