Applied Text Mining and Sentiment Analysis with Python

Perform Sentiment Analysis on Twitter data by combining Text Mining and NLP techniques, NLTK and Scikit-Learn

4.22 (691 reviews)

Udemy

platform

English

language

Data Science

What you will learn

How to use common Text Mining and NLP techniques

How to use Regex to clean up Tweets

How to use NLTK to pre-process text

How to use Scikit-Learn to build a Sentiment Analysis prediction model

How to predict the sentiment of any tweet

Course Gallery

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Comidoc Review

Our Verdict

Applied Text Mining and Sentiment Analysis with Python course offers a solid foundation in fundamental text mining techniques, data pre-processing using Regex and NLTK, and hands-on experiences. Some learners expressed the need for better guidance in obtaining datasets from Twitter and managing subtitles issues. A few faced challenges related to code execution due to differences in Python versions. However, this course can aid graduation projects and improve sentiment analysis understanding.

What We Liked

Covers fundamental Text Mining and NLP techniques for Sentiment Analysis
In-depth explanations of data pre-processing using Regex, NLTK
Hands-on course with real-world examples and user-made functions
Helped learners in their graduation projects and understanding SA concepts

Potential Drawbacks

Lacks guidance on directly obtaining datasets from Twitter
Some issues with subtitles causing confusion for a few
Occasional challenges with code execution due to Python version differences
Minimal coverage of Sentiment Analysis beyond the specific techniques used