Frame Analysis requires sophisticated semantic analysis, filtering, situational understanding, and inference on missing text. Near as I can tell, this level of sophistication is beyond the grasp of the common NLP and text mining tools. Is this true? If not, do any of you know of fully automated tools for Frame Analysis?
I should add that I have two use cases. First, the most demanding, is automatic identification of frames followed by text classification. Second, more feasible, is automatic classification of texts given frame definitions and sample texts. The latter fits the classic machine learning model of supervised learning, so I assume that as long as my training set is large enough and representative enough, I can probably find an adequate ML classification algorithm.
[Edit 7/23/2013: This is the best summary I could find of available tools: Frame Analysis: Software]
Goffman, E. (1976). Frame Analysis: An Essay on the Organization of Experience. Harvard University Press.
Johnston, Hank (1995). A methodology for frame analysis: from discourse to cognitive schemata. In Social Movements and Culture (pp. 217–246). University of Minnesota Press.
Nisbet, M. C. (2009). Communicating Climate Change: Why Frames Matter for Public Engagement, Environment. Science and Policy for Sustainable Development, 51(2): 12–23.