Building Sentiment Analysis Systems in Python
Pluralsight
Course Summary
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.
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Course Description
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.
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Course Syllabus
Course Overview- 1m 39s
—Course Overview 1m 39sIdentifying Applications of Sentiment Analysis- 29m 33s
—Introducing Sentiment Analysis 5m 56s
—Two Case Studies 6m 52s
—Polarity Detection for Sentiment Analysis 5m 44s
—Setting up a Binary Classification Problem 3m 38s
—Rule-based and ML-based Binary Classifiers 7m 21sSolving Sentiment Analysis with a Rule-based Approach- 26m 42sImplementing​ Sentiment Analysis with a Rule-based Approach- 36m 31sSolving Sentiment Analysis with an ML Based Approach- 28m 29sImplementing Sentiment Analysis with an ML Based Approach- 28m 55s
