1/15/18

• Python skills development - we will be doing a lot with Python, so complete the following:
  • Create an account at Computer Science Circles, complete lessons 0 thru 18, then show your progress page to Dr. Way when done
  • Supplement your learning at any time with exercises at LearnPython.org
• Review Sentiment Analysis materials:
  • Sentiment Analysis (Wikipedia article)
  • What is Sentiment Analysis (Stanford NLP open course) (slides)

1/29/18

• Setup research & development environment
  • Install Python 3 and NLTK
  • Explore to online resources at NLTK.org
  • Try your hand at implementing sentiment analysis (PythonSpot.com)

2/07/18

• Planning meeting  (2:30 pm, CS dept lunch room)
  • Caitlin Berner, Matt Mador in attendance. Matt Krause updated via email.
  • Discussed short-term plans
  • Once initial Python tutorial is complete, students will each develop a static sentiment analyzer
  • After that, additional enhancements will be implemented, selected from Project Ideas list
  • Next meeting TBD. Possible date Thur. March 1, 2018 at 10:00 am
• Project Ideas
  • Static sentiment analyzer - use a polarity dictionary that contains positive and negative words, run some input text through the program and calculate overall sentiment using the dictionary
  • Twitter analyzer - use static sentiment analyzer on acquired Twitter data
  • Analyzer comparison - implement two or more simple sentiment analyzers using approaches found in online searches, compare for accuracy
  • Retargetable analyzer - find a different polarity dictionary that classifies sentiment in different ways (liberal vs. conservative, pro vs. anti some position, degrees of happiness, etc.) and report on its effectiveness
  • Assisted improvable analyzer - use machine learning to build or enhance a polarity dictionary, for example adding additional polarity terms to the dictionary when a sentiment is “correct” according to a human supervisor.
  • Automatic improvable analyzer - figure out how to get the improvable analyzer to learn automatically.
  • Polarity dictionary builder - craft a way to harvest polarity terms, perhaps using online searches, and determine if this approach is feasible.

3/01/18

• Project meeting and demostration (10:30am, Dr. Way's office)
• Currently developed version using pre-coded, small lists of polarity words (pos, neu, neg)
• Next steps discussed are:
  • Find and use sentiment dictionaries to train the classifier (read from text files, use lists of pos, neu and neg words)
  • Find or create content to analyze, such as a text file of tweets or movie reviewes (one on each line) or from an NLTK corpus
• Next meeting: tentatively 3/20/18 at 3:00 pm

3/12/18

• Check-in, report progress, help with any roadblocks

3/20/18

• Meeting

3/26/18

• Development
• Evaluation

4/09/18

• Select final project (enhancements to earlier projects)
• Development

4/23/18

• Development
• Results write-up
• Evaluation

Final

• Submission of completed research work