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.

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.

• Course Overview 1min
• Identifying Applications of Sentiment Analysis 29mins
• Solving Sentiment Analysis with a Rule-based Approach 26mins
• Implementing​ Sentiment Analysis with a Rule-based Approach 36mins
• Solving Sentiment Analysis with an ML Based Approach 28mins
• Implementing Sentiment Analysis with an ML Based Approach 28mins