Ashley, You might want to chat with the Health Media Collaboratory http://www.healthmediacollaboratory.org/ They have done interesting work with our tools: https://vimeo.com/55175059 We interviewed researchers at the University of Illinois Chicago in the Health Media Collaboratory about their use of DiscoverText and the Gnip-enabled Power Track for Twitter to study smoking behavior. The team, led by Dr. Sherry Emery, explains why it is important to train and use custom machine classifiers to sort the millions of tweets they are collecting from the full Twitter fire hose. The UIC team strongly argues for the combination of good tools and highly reliable data. On Fri, Feb 14, 2014 at 10:34 AM, Ashley Nicoles Sanders-Jackson <asnsande@stanford.edu> wrote:

Greetings,

I am beginning a project for which we have 4 private smoking cessation groups (3 months each) to analyze (we are writing a grant to collect more data as well). I have seen some of the work related to sentiment analysis on Twitter but I am interested in developing a system that is better tailored to our data (e.g. being smokefree has specific meaning in this context). We are therefore considering developing a system by content coding a number of tweets (either having researchers code them or by having smokers code them) for positive and negative valence and perhaps some discrete emotions (e.g. sadness or hope). How many coded tweets would we need to train a simple machine learning system on our dataset (for example one of the many possibilities in R) and what are the best out-of-box programs to use? I know a bit about content analysis and about smoking cessation but not so much about machine learning. So bear in mind that you are dealing with a novice. Actually, if anyone would be interested in collaborating on the project who actually does know what they are doing, they would be welcome as well.

Ashley _______________________________________________ The Air-L@listserv.aoir.org mailing list is provided by the Association of Internet Researchers http://aoir.org Subscribe, change options or unsubscribe at: http://listserv.aoir.org/listinfo.cgi/air-l-aoir.org

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