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Text Analysis Fundamentals: Supervised Methods [D-Lab]

October 17 @ 12:00 pm - 2:00 pm

See organizers’ website for details and registration.

In this workshop we will cover two main supervised text analysis methods, the dictionary method, and supervised classification. We will use list comprehension to implement the dictionary method, using sentiment analysis as our example. Using the Python library scikit-learn, we will also implement a few supervised classification techniques, including Naive Bayes and Support Vector Machines. Specific skills covered include a) measuring themes in text using dictionaries, b) feature selection, c) Support Vector Machines, d) Naive Bayes, e) cross-validation, and f) feature importance.

Prior knowedlge: Basic familiarity with Python is required if you wish to follow along with the tutorial. Completion of D-Lab’s Python FUN!damentals workshop series will be sufficient.

Details

Date:
October 17
Time:
12:00 pm - 2:00 pm
Event Category:
Website:
http://dlab.berkeley.edu/training/text-analysis-fundamentals-supervised-methods-4

Organizer

D-Lab

Venue

356 Barrows Hall
Barrows Hall
Berkeley, CA 94720 United States
+ Google Map
Website:
http://www.berkeley.edu/map?barrows