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Byte-Sized-Chunks: Twitter Sentiment Analysis (in Python)
Rating: 3.8 out of 5(97 ratings)
2,832 students

Byte-Sized-Chunks: Twitter Sentiment Analysis (in Python)

Use Python and the Twitter API to build your own sentiment analyzer!
Created byLoony Corn
Last updated 3/2016
English

What you'll 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

Course content

1 section • 14 lectures • 3h 14m total length

  • You, This Course, and Us!1:24
  • A Sneak Peek at what's coming up2:36
    Lots of new stuff coming up in the next few classes. Sentiment Analysis (or) Opinion Mining is a field of NLP that deals with extracting subjective information (positive/negative, like/dislike, emotions). Learn why it's useful and how to approach the problem. There are Rule-Based and ML-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.
  • Sentiment Analysis - What's all the fuss about?17:17

    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)

  • ML Solutions for Sentiment Analysis - the devil is in the details19:57

    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.

  • Sentiment Lexicons ( with an introduction to WordNet and SentiWordNet)18:49
    Sentiment Lexicon's are a great help in solving problems where the subjectivity/emotion expressed by a word are important. SentiWordNet is different even among the popular sentiment lexicons (General Inquirer, LIWC, MPQA etc) all of which are touched upon
  • Installing Python - Anaconda and Pip9:00

    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'

  • Back to Basics : Numpy in Python18:05
    Numpy arrays are pretty cool for performing mathematical computations on your data.
  • Back to Basics : Numpy and Scipy in Python14:19
    We continue with a basic tutorial on Numpy and Scipy
  • Regular Expressions17:53

    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.

  • Regular Expressions in Python5:41

    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.

  • Put it to work : Twitter Sentiment Analysis17:48

    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 :)

  • Twitter Sentiment Analysis - Work the API20:00
    We'll accept a search term from a user and download a 100 tweets with that term. You'll need a corpus to train a classifier which can classifiy these tweets. The corpus has only tweet_ids, so connect to Twitter API and fetch the text for the tweets.
  • Twitter Sentiment Analysis - Regular Expressions for Preprocessing12:24

    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.

  • Twitter Sentiment Analysis - Naive Bayes, SVM and Sentiwordnet19:40

    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.

Requirements

  • No prerequisites, knowledge of some undergraduate level mathematics would help but is not mandatory. Working knowledge of Python would be helpful if you want to perform the coding exercise and understand the provided source code

Description

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).

  • Learn why it's useful and how to approach the problem: Both Rule-Based and ML-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.

Sentiment Analysis:

  • Why it's useful,
  • Approaches to solving - Rule-Based , ML-Based
  • Training & Feature Extraction
  • Sentiment Lexicons
  • Regular Expressions
  • Twitter API
  • Sentiment Analysis of Tweets with Python

Who this course is for:

  • Nope! Please don't enroll for this class if you have already enrolled for our 21-hour course 'From 0 to 1: Machine Learning and NLP in Python'
  • Yep! Analytics professionals, modelers, big data professionals who haven't had exposure to machine learning
  • Yep! Engineers who want to understand or learn machine learning and apply it to problems they are solving
  • Yep! Tech executives and investors who are interested in big data, machine learning or natural language processing
  • Yep! Product managers who want to have intelligent conversations with data scientists and engineers about machine learning

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