{"action":"opened","issue":{"url":"https://api.github.com/repos/Computational-Content-Analysis-2018/9-Feb-A-Method-of-Automated-Nonparametric-Content-Analysis-for-Social-Science/issues/19","repository_url":"https://api.github.com/repos/Computational-Content-Analysis-2018/9-Feb-A-Method-of-Automated-Nonparametric-Content-Analysis-for-Social-Science","labels_url":"https://api.github.com/repos/Computational-Content-Analysis-2018/9-Feb-A-Method-of-Automated-Nonparametric-Content-Analysis-for-Social-Science/issues/19/labels{/name}","comments_url":"https://api.github.com/repos/Computational-Content-Analysis-2018/9-Feb-A-Method-of-Automated-Nonparametric-Content-Analysis-for-Social-Science/issues/19/comments","events_url":"https://api.github.com/repos/Computational-Content-Analysis-2018/9-Feb-A-Method-of-Automated-Nonparametric-Content-Analysis-for-Social-Science/issues/19/events","html_url":"https://github.com/Computational-Content-Analysis-2018/9-Feb-A-Method-of-Automated-Nonparametric-Content-Analysis-for-Social-Science/issues/19","id":295944672,"number":19,"title":"size of corpus","user":{"login":"jgdenby","id":29633570,"avatar_url":"https://avatars0.githubusercontent.com/u/29633570?v=4","gravatar_id":"","url":"https://api.github.com/users/jgdenby","html_url":"https://github.com/jgdenby","followers_url":"https://api.github.com/users/jgdenby/followers","following_url":"https://api.github.com/users/jgdenby/following{/other_user}","gists_url":"https://api.github.com/users/jgdenby/gists{/gist_id}","starred_url":"https://api.github.com/users/jgdenby/starred{/owner}{/repo}","subscriptions_url":"https://api.github.com/users/jgdenby/subscriptions","organizations_url":"https://api.github.com/users/jgdenby/orgs","repos_url":"https://api.github.com/users/jgdenby/repos","events_url":"https://api.github.com/users/jgdenby/events{/privacy}","received_events_url":"https://api.github.com/users/jgdenby/received_events","type":"User","site_admin":false},"labels":[],"state":"open","locked":false,"assignee":null,"assignees":[],"milestone":null,"comments":0,"created_at":"2018-02-09T17:14:59Z","updated_at":"2018-02-09T17:14:59Z","closed_at":null,"author_association":"NONE","body":"this methodology seems particularly well-suited for coarse-grained estimates of large corpora with many documents (i.e., \"characterizing the haystack\"), but how useful is it with smaller corpora? Or, could these methods be used to determine the make-up of, e.g., paragraphs in a handful of documents? at what point do the model's assumptions (e.g., mutual exclusivity of categories, homogeneity of probability distributions, etc.) become too stringent?"}}
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