{"action":"opened","issue":{"url":"https://api.github.com/repos/dataworks/internship-2016/issues/83","repository_url":"https://api.github.com/repos/dataworks/internship-2016","labels_url":"https://api.github.com/repos/dataworks/internship-2016/issues/83/labels{/name}","comments_url":"https://api.github.com/repos/dataworks/internship-2016/issues/83/comments","events_url":"https://api.github.com/repos/dataworks/internship-2016/issues/83/events","html_url":"https://github.com/dataworks/internship-2016/issues/83","id":162917191,"number":83,"title":"Add text classification feature to ML Job","user":{"login":"davidmezzetti","id":561939,"avatar_url":"https://avatars.githubusercontent.com/u/561939?v=3","gravatar_id":"","url":"https://api.github.com/users/davidmezzetti","html_url":"https://github.com/davidmezzetti","followers_url":"https://api.github.com/users/davidmezzetti/followers","following_url":"https://api.github.com/users/davidmezzetti/following{/other_user}","gists_url":"https://api.github.com/users/davidmezzetti/gists{/gist_id}","starred_url":"https://api.github.com/users/davidmezzetti/starred{/owner}{/repo}","subscriptions_url":"https://api.github.com/users/davidmezzetti/subscriptions","organizations_url":"https://api.github.com/users/davidmezzetti/orgs","repos_url":"https://api.github.com/users/davidmezzetti/repos","events_url":"https://api.github.com/users/davidmezzetti/events{/privacy}","received_events_url":"https://api.github.com/users/davidmezzetti/received_events","type":"User","site_admin":false},"labels":[],"state":"open","locked":false,"assignee":null,"assignees":[],"milestone":null,"comments":0,"created_at":"2016-06-29T12:51:31Z","updated_at":"2016-06-29T12:51:31Z","closed_at":null,"body":"**Overview**\r\nIn the most successful ML projects, you will often build an ensemble of models. The results can be combined differently or even used as a feature in another model. This task here is to look into building a new feature for our existing model to label documents based on the text content.\r\n\r\n**Hypothesis**\r\nFavorite resumes are more likely to have similar terms and archived resumes are more likely to have similar terms.\r\n\r\n**Implementation Strategies**\r\nThere are a number of ways to do this in increasing order of complexity. Start with the first one and go on to others only if necessary.\r\n\r\n1.) Simple Bayes classifier. Similar to a spam filter. Looking for common words that are in your two labels and giving the likelihood of it being a class. Example of doing this can be found here: https://github.com/dataworks/internship-2015/tree/master/etl/Cleanser\r\n\r\n2.) Paragraph vectors - Using dl4j/deep learning to classify. Example can be found here: https://github.com/kogecoo/dl4j-0.4-examples-scala/blob/master/src/main/scala/org/deeplearning4j/examples/nlp/paragraphvectors/ParagraphVectorsClassifierExample.scala\r\n\r\n3.) Word2VecRNN - Combine word vectors and a recurrent neural network. Would be a project within itself. Example can be found here: https://github.com/kogecoo/dl4j-0.4-examples-scala/tree/master/src/main/scala/org/deeplearning4j/examples/recurrent/word2vecsentiment"}}
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