{"action":"opened","issue":{"url":"https://api.github.com/repos/RasaHQ/rasa_nlu/issues/425","repository_url":"https://api.github.com/repos/RasaHQ/rasa_nlu","labels_url":"https://api.github.com/repos/RasaHQ/rasa_nlu/issues/425/labels{/name}","comments_url":"https://api.github.com/repos/RasaHQ/rasa_nlu/issues/425/comments","events_url":"https://api.github.com/repos/RasaHQ/rasa_nlu/issues/425/events","html_url":"https://github.com/RasaHQ/rasa_nlu/issues/425","id":234825821,"number":425,"title":"Sentiment Analysis in Rasa","user":{"login":"znat","id":3694634,"avatar_url":"https://avatars3.githubusercontent.com/u/3694634?v=3","gravatar_id":"","url":"https://api.github.com/users/znat","html_url":"https://github.com/znat","followers_url":"https://api.github.com/users/znat/followers","following_url":"https://api.github.com/users/znat/following{/other_user}","gists_url":"https://api.github.com/users/znat/gists{/gist_id}","starred_url":"https://api.github.com/users/znat/starred{/owner}{/repo}","subscriptions_url":"https://api.github.com/users/znat/subscriptions","organizations_url":"https://api.github.com/users/znat/orgs","repos_url":"https://api.github.com/users/znat/repos","events_url":"https://api.github.com/users/znat/events{/privacy}","received_events_url":"https://api.github.com/users/znat/received_events","type":"User","site_admin":false},"labels":[],"state":"open","locked":false,"assignee":null,"assignees":[],"milestone":null,"comments":0,"created_at":"2017-06-09T13:24:50Z","updated_at":"2017-06-09T13:24:50Z","closed_at":null,"body":"Would be really great to have that and I'm interested in contributing. For example, I like the way [recast.ai](https://recast.ai/) adds sentiment to any query.\r\n\r\nThis is to start the discussion: what should it be? \r\nShould it be a (1) pre-trained per language model with state of the art results like models trained on the Stanford sentiment tree bank, or a (2) trained in Rasa model? (a sentiment label would be added to each example fed to Rasa at training time).\r\n\r\n(1) would probably complicate the stack a lot (recursive networks not well supported in DL frameworks) but there a not state of the art models that are is still good and easy to implement in Keras. But does it make sense to add DL to the Rasa stack?\r\n\r\n(2) seems easier as I suppose decent results on small datasets can be obtained with algos supported by sklearn. Probably lower accuracy but it's also domain specific so it may be good enough. \r\n\r\nWhat are your thoughts?\r\n\r\n\r\n\r\n\r\n \r\n"}}
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