Byte-Sized-Chunks: Twitter Sentiment Analysis (in Python)
- 3.9
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3.5 hours on-demand video
$
9.99
Brief Introduction
Use Python and the Twitter API to build your own sentiment analyzer!Description
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
Requirements
- Requirements
- No prerequisites, 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
Loony Corn
3.9 Raiting
Byte-Sized-Chunks: Twitter Sentiment Analysis (in Python)
- 3.9
-
![]()
![]()
![]()
![]()
![]()
Use Python and the Twitter API to build your own sentiment analyzer! Learn why it's useful and how to approach the problem: Both Rule-Based and ML-Based approaches....
