Burke County Public Schools

Learn why Sentiment Analysis is useful and how to approach the problem using both Rule-Based and Machine Learning-Based approaches. The details are really important - training data and feature extraction are critical. Sentiment Lexicons provide us with lists of words in different sentiment categories that we can use for building our feature set. All this is in the run up to a serious project to perform Twitter Sentiment Analysis. We’ll spend some time on Regular Expressions which are pretty handy to know as we’ll see in our code-along.

What Will I Learn?

• Design and Implement a sentiment analysis measurement system in Python
• Grasp the theory underlying sentiment analysis, and its relation to binary classification
• Identify use-cases for sentiment analysis
• Learn about Sentiment Lexicons, Regular Expressions & Twitter API

Requirements:
No prerequisites required, however knowledge of some undergraduate level mathematics would help, but is not mandatory. Working knowledge of Python would be helpful if you want to perform the coding exercise and understand the provided source code.

Who is the target audience?

• Analytics professionals, modelers, big data professionals who haven’t had exposure to machine learning
• Engineers who want to understand or learn machine learning and apply it to problems they are solving
• Tech executives and investors who are interested in big data, machine learning or natural language processing
• Product managers who want to have intelligent conversations with data scientists and engineers about machine learning