Zoom Link: https://berkeley.zoom.us/s/92943664436
In this workshop we will cover the most common CTA task: supervised classification. Using the Python library scikit-learn, we will implement Logistic Regression and Random Forest methods to perform sentiment analysis. Optional: introduction to word vector representations with Word2Vec.
Prior knowledge: 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.
This workshop is one of a three-part series that will prepare participants to move forward with text analysis research. Please register for each workshop separately.
- Text Analysis Fundamentals: Basic Tools and Techniques
- Text Analysis Fundamentals: Unsupervised Approaches
Getting started & software prerequisites:
We will learn how to imple ment text analysis methods with Jupyter Notebooks.
To run the code on your computer, you will need to have Python 3 installed as well as some additional libraries. Anaconda is a free product that makes the installation process easy. It bundles together the Python language and a whole bunch of additional packages that we often rely on in our workshops. This way, you only have to download and install one thing. To use this method, visit this site and follow the instructions for your operating system to download the Python 3.x version (it might be 3.6, or 3.7, or higher). Please, please, please download the 3.x version, not the Python 2.x version. You may have a choice between using the graphical installer or the command line installer. Use whichever you're comfortable with, but the graphical one is easier.
IMPORTANT: Please download the material for day 3 using the link below and save the folder on your desktop . The content may change between workshops so make sure you havsdfsdfe downloaded the most recent version before each workshop.
