This project will explore the application of Natural Language Processing (NLP) and Machine Learning (ML) tools to the context domain of organizational behaviour, more specifically to a study of group maintenance in a novel setting. The project involves information scientists working collaboratively with domain scientists with the goal of developing an innovative NLP and ML-based research tool to support qualitative social science research, specifically content analysis.
Submitted by Nancy McCracken on Wed, 2013-08-21 12:37
Undergraduate Research Intern positions available for the academic year on-campus (fall 2013 and spring 2014). These positions are funded under the Research Experiences for Undergraduates (REU) program from the NSF and provide an $8,000 stipend paid over the academic year. Undergraduate students from information science, the social sciences and computer science who are interested in participating in an interdisciplinary research team are encouraged to apply by September 9, 2013.
Submitted by Nancy McCracken on Fri, 2013-06-28 14:31
We have recently completed most of the functionality of the SoCQA tool. This includes ingesting the documents as annotated in Atlas-ti, learning a model from that data and reporting the model performance results, applying the model to additional data and allowing the user to verify whether the model predictions are correct or not.
We're near the end of the 1st year of the grant and system development is progressing well, albeit a bit slower than we'd hoped. The high level system design is set and we've been implementing functionality in a series of sprints. By the end of the current sprint, we should have a basic system in place, allowing us to import email messages, import human annotations of some of the data from an Atlas-ti file, learn a model and apply the model to additional data. The final piece will be an interface to allow a human coder to correct the machine-applied annotations.
This work was partially supported by a grant from the US National Science Foundation Socio-computational Systems (SOCS) program, Grant 11–11107. Any opinions, findings, and conclusions or recommendations expressed in this material are those of the author(s) and do not necessarily reflect the views of the National Science Foundation.