Use Python & the Twitter API to Build Your Own Sentiment Analyzer.
Sentiment Analysis, or Opinion Mining, is a field of Neuro-linguistic Programming that deals with extracting subjective information, like positive/negative, like/dislike, and emotional reactions. In this “Twitter Sentiment Analysis in Python” online course, you’ll learn real examples of why Sentiment Analysis is important and how to approach specific problems using Sentiment Analysis. Supplemental Material included!
Learn why Sentiment Analysis is useful and how to approach the problem using both Rule-Based and Machine Learning-Based approaches. The details are really important – training data and feature extraction are critical. Sentiment Lexicons provide us with lists of words in different sentiment categories that we can use for building our feature set. All this is in the run up to a serious project to perform Twitter Sentiment Analysis. We’ll spend some time on Regular Expressions which are pretty handy to know as we’ll see in our code-along.
What Will I Learn?
- Design and Implement a sentiment analysis measurement system in Python
- Grasp the theory underlying sentiment analysis, and its relation to binary classification
- Identify use-cases for sentiment analysis
- Learn about Sentiment Lexicons, Regular Expressions & Twitter API
Chapter I: What are You Feeling Like?
- Lesson I: Introduction: You, This Course & Us!
- Lesson II: Sentiment Analysis: What’s all the fuss about?
- Lesson III: Machine Learning Solutions for Sentiment Analysis: the devil is in the details
- Lesson IV: Sentiment Lexicons (with an introduction to WordNet and SentiWordNet)
- Lesson V: Installing Python – Anaconda and Pip
- Lesson VI: Back to Basics: Numpy in Python
- Lesson VII: Back to Basics: Numpy & Scipy in Python
- Lesson VIII: Regular Expressions
- Lesson IX: Regular Expressions in Python
- Lesson X: Put it to work: Twitter Sentiment Analysis
- Lesson XI: Twitter Sentiment Analysis: Work the API
- Lesson XII: Twitter Sentiment Analysis: Regular Expressions for Preprocessing
- Lesson XIII: Twitter Sentiment Analysis: Naive Bayes, SVM & SentiWordNet
Janani Ravi, Vitthal Srinivasan, Swetha Kolalapudi, and Navdeep Singh have honed their tech expertise at Google and Flipkart. Together, they have created dozens of training courses and are excited to be sharing their content with eager students. The team believes it has distilled the instruction of complicated tech concepts into enjoyable, practical, and engaging courses.
Janani: 7 years at Google (New York, Singapore); Studied at Stanford; also worked at Flipkart and Microsoft
Vitthal: Also Google (Singapore) and studied at Stanford; Flipkart, Credit Suisse and INSEAD too
Swetha: Early Flipkart employee, IIM Ahmedabad and IIT Madras alum
Navdeep: Longtime Flipkart employee too, and IIT Guwahati alum
Frequently Asked Questions
Who is the target audience?
- Analytics professionals, modelers, big data professionals who haven’t had exposure to machine learning
- Engineers who want to understand or learn machine learning and apply it to problems they are solving
- Tech executives and investors who are interested in big data, machine learning or natural language processing
- Product managers who want to have intelligent conversations with data scientists and engineers about machine learning
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