Note: This course is a subset of our 20+ hour course 'From 0 to 1: Machine Learning & Natural Language Processing' so please don't sign up for both:-)
Sentiment Analysis (or) Opinion Mining is a field of NLP that deals with extracting subjective information (positive/negative, like/dislike, emotions).
Using discussion forums
Please use the discussion forums on this course to engage with other students and to help each other out. Unfortunately, much as we would like to, it is not possible for us at Loonycorn to respond to individual questions from students:-(
We're super small and self-funded with only 2 people developing technical video content. Our mission is to make high-quality courses available at super low prices.
The only way to keep our prices this low is to *NOT offer additional technical support over email or in-person*. The truth is, direct support is hugely expensive and just does not scale.
We understand that this is not ideal and that a lot of students might benefit from this additional support. Hiring resources for additional support would make our offering much more expensive, thus defeating our original purpose.
It is a hard trade-off.
Thank you for your patience and understanding!
As people spend more and more time on the internet, and the influence of social media explodes, knowing what your customers are saying about you online, becomes crucial. Sentiment Analysis comes in handy here - This is an NLP problem that can be approached in multiple ways. We examine a couple of rule based approaches, one of which has become standard fare (VADER)
SVM and Naive Bayes are popular ML approaches to Sentiment Analysis. But the devil really is in the details. What do you use for training data? What features should you use? Getting these right is critical.
Anaconda's iPython is a Python IDE. The best part about it is the ease with which one can install packages in iPython - 1 line is virtually always enough. Just say '!pip'
Regular expressions are a handy tool to have when you deal with text processing. They are a bit arcane, but pretty useful in the right situation. Understanding the operators from basics help you build up to constructing complex regexps.
re is the module in python to deal with regular expressions. It has functions to find a pattern, substitute a pattern etc within a string.
A serious project - Accept a search term from a user and output the prevailing sentiment on Twitter for that search term. We'll use the Twitter API, Sentiwordnet, SVM, NLTK, Regular Expressions - really work that coding muscle :)
The tweets that we downloaded have a lot of garbage, clean it up using regular expressions and NLTK and get a nice list of words to represent each tweet.
We'll train 2 different classifiers on our training data , Naive Bayes and SVM. The SVM will use Sentiwordnet to assign weights to the elements of the feature vector.
Loonycorn is us, Janani Ravi and Vitthal Srinivasan. Between us, we have studied at Stanford, been admitted to IIM Ahmedabad and have spent years working in tech, in the Bay Area, New York, Singapore and Bangalore.
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
We think we might have hit upon a neat way of teaching complicated tech courses in a funny, practical, engaging way, which is why we are so excited to be here on Udemy!
We hope you will try our offerings, and think you'll like them :-)