Course Project

A major aspect of the course is the project, which will be developed in six phases, alternating with the homework assignments.

• Phase 1: Using the Twitter streaming API and doing basic processing of tweets.
• Phase 2: Using twitter4j to process tweets using Scala.
• Phase 3: Create interactive Twitter applications (e.g. bots and/or clusterers)
• Phase 4: Extended bot behavior w/ Akka or data analysis and visualization.
• Phase 5: Refine and extend the work from Phase 4.
• Project Presentation: End-of-semester presentation of the course project.
• Project Report: The end-of-semester final report on the course project.

The semester is over, and the projects are finished! Here are the papers, PDFs and links to the code.

• Karthik Anantha Padmanabhan. 2013. TF-Pundit: A Real-time Football Pundit based on Twitter. UT Austin Applied NLP Course Project, Spring 2013. code
• Cuong Chau. 2013. An Automated Interaction Application on Twitter. UT Austin Applied NLP Course Project, Spring 2013. code
• Jim Evans and Jason Mielens. 2013. Tshrdlu Expanded. UT Austin Applied NLP Course Project, Spring 2013. code
• Anne Flinchbaugh and Eric Latimer. 2013. ReelTalk: An Interactive Sentiment Analysis Application. UT Austin Applied NLP Course Project, Spring 2013. code
• John Matthew Fong and Hassaan Markhiani. 2013. Twitter Stock Bot UT Austin Applied NLP Course Project, Spring 2013. code
• Simon Hafner. 2013. Tweets about "scala", but not about "scala". (PDF to be added) UT Austin Applied NLP Course Project, Spring 2013. code
• Stephen Pryor and Joseph Perez. 2013. dash4twitter: A Dashboard for Twitter Analytics. UT Austin Applied NLP Course Project, Spring 2013. code Demo1 Demo2
• Nick Wilson and Nazneen Rajani. 2013. Generating Twitter Replies Based on User Location. UT Austin Applied NLP Course Project, Spring 2013. code

Please cite the relevant paper if you use it or any of the resources!