Byte-Sized-Chunks: Twitter Sentiment Analysis (in Python)

Byte-Sized-Chunks: Twitter Sentiment Analysis (in Python)

Note: This course is a subset of our 20+ hour course 'From 0 to 1: Machine Learning & Natural Language Processing' so please don't sign up for both:-)

Sentiment Analysis (or) Opinion Mining is a field of NLP that deals with extracting subjective information (positive/negative, like/dislike, emotions).

• Learn why it's useful and how to approach the problem: Both Rule-Based and ML-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.

Sentiment Analysis:

• Why it's useful,
• Approaches to solving - Rule-Based , ML-Based
• Training & Feature Extraction
• Sentiment Lexicons
• Regular Expressions
• Twitter API
• Sentiment Analysis of Tweets with Python

Use Python and the Twitter API to build your own sentiment analyzer!

Url: View Details

What you will 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

Rating: 4.15

Level: All Levels

Duration: 3.5 hours

Instructor: Loony Corn