Natural Language Processing

The use of natural language processing has exploded over the last decade. Appilcations that require machines to understand natural human speech patterns are abundant and substantial improvements in these systems has increased their utility. Within the educational space NLP is used to interpret human speech for the prupose of understanding human problems and recently an online tutor passed a limited version of the Turing Test when it was indistinguishable from teaching assistants in a college class.

• Processed a set of documents of student notes that the class HUDK4050 made last semester
• Ran a sentiment analysis of thise documents
• Generated a topic model of those documents

Goals

• Understand and apply the basic process of natural language proceessing and the role of pre-processing text
• Understand and apply a simple sentiment analysis to a set of documents
• Understand and apply a latent dirichlet allocation to a set of documents

Utilized the following resources

Video Slide Deck

Crash Course. (2017). Natural Language Processing.

Raval, S. (2016). Sentiment Analysis in 4 Minutes.

Knispelis, A. (2016). LDA Topic Models.

Nadkarni, P. M., Ohno-Machado, L., & Chapman, W. W. (2011). Natural language processing: An Introduction. Journal of the American Medical Informatics Association: JAMIA, 18(5), 544–551.

Robinson, A. C. (2015). Exploring Class Discussions from a Massive Open Online Course (MOOC) on Cartography. In J. Brus, A. Vondrakova, & V. Vozenilek (Eds.), Modern Trends in Cartography (pp. 173–182). Springer International Publishing.

McNamara, D. S., Crossley, S. A., & Roscoe, R. (2013). Natural Language Processing in an Intelligent Writing Strategy Tutoring System. Behavior Research Methods, 45(2), 499–515.

Quora. (2017). What is a good explanation of Latent Dirichlet Allocation?