# What's In The Deep-NLP Dataset? Sheet_1.csv contains 80 user responses, in the response_text column, to a therapy chatbot. Bot said: 'Describe a time when you have acted as a resource for someone else'.  User responded. If a response is 'not flagged', the user can continue talking to the bot. If it is 'flagged', the user is referred to help. Sheet_2.csv contains 125 resumes, in the resume_text column. Resumes were queried from Indeed.com with keyword 'data scientist', location 'Vermont'. If a resume is 'not flagged', the applicant can submit a modified resume version at a later date. If it is 'flagged', the applicant is invited to interview. # What Do I Do With This? Classify new resumes/responses as flagged or not flagged. There are two sets of data here - resumes and responses. Split the data into a train set and a test set to test the accuracy of your classifier. Bonus points for using the same classifier for both problems. Good luck. # Acknowledgements Thank you to [Parsa Ghaffari][1] (Aylien), without whom these visuals (cover photo is in Parsa Ghaffari's excellent LinkedIn [article][2] on English, Spanish and German postive v. negative sentiment analysis) would not exist. # There Is A 'deep natural language processing' Kernel. I will update it. I Hope You Find It Useful. You can use any of the code in that kernel anywhere, on or off Kaggle. Ping me at [@_samputnam][3] for questions. [1]: http://aylien.com [2]: https://www.linkedin.com/pulse/leveraging-deep-learning-multilingual-sentiment-parsa-ghaffari [3]: http://twitter.com/_samputnam