Building Sentiment Analysis Systems in Python
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.
Table of contents
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