Simple play icon Course
Skills Expanded

Building Sentiment Analysis Systems in Python

by Vitthal Srinivasan

Sentiment Analysis has become increasingly important as more opinions are expressed online, in unstructured form. This course covers rule-based and ML-based approaches to extracting sentiment from opinions, including VADER, Sentiwordnet, and more.

What you'll learn

Online opinions are becoming ubiquitous - more people are expressing their views online than ever before. As a result, extracting sentiment information from these opinions is becoming very important. In this course, Building Sentiment Analysis Systems in Python, you will learn the fundamentals of building a system to do so in Python. First, you will learn the differences between ML- and rule-based approaches, and how to use VADER, Sentiwordnet, and Naive Bayes classifiers. Next, you will build three sentiment analyzers, and use them to classify a corpus of movie reviews made available by Cornell. Finally, you will gain a conceptual understanding of Support Vector Machines, and why Naive Bayes is usually a better choice. When you're finished with this course, you will have a clear understanding of how to extract sentiment from a body of opinions, and of the design choices and trade-offs involved.

About the author

Vitthal has spent a lot of his life studying - he holds Masters Degrees in Math and Electrical Engineering from Stanford, an MBA from INSEAD, and a Bachelors Degree in Computer Engineering from Mumbai. He has also spent a lot of his life working - as a derivatives quant at Credit Suisse in New York, then as a quant trader, first with a hedge fund in Greenwich and then on his own, and finally at Google in Singapore and Flipkart in Bangalore. In all these roles, he has written a lot of code, and b... more

Ready to upskill? Get started