Sentiment Analysis in Python

Are customers thrilled with your products or is your service lacking? Learn how to perform an end-to-end sentiment analysis task.

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4 Hours16 Videos60 Exercises8,869 Learners
5050 XP

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Course Description

Have you left a review to express how you feel about a product or a service? And do you have a habit of checking a product’s reviews online before you buy it? This kind of information is valuable not only for you but also for companies. In this course, you will learn how to make sense of the sentiment expressed in various documents. You will use real-world datasets featuring tweets, movie and product reviews, and use Python’s nltk and scikit-learn packages. By the end of the course, you will be able to carry an end-to-end sentiment analysis task based on how US airline passengers expressed their feelings on Twitter.

  1. 1

    Sentiment Analysis Nuts and Bolts

    Free

    Have you ever checked the reviews or ratings of a product or a service before you purchased it? Then you have very likely came face-to-face with sentiment analysis. In this chapter, you will learn the basic structure of a sentiment analysis problem and start exploring the sentiment of movie reviews.

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    Welcome!
    50 xp
    Elements of a sentiment analysis problem
    50 xp
    How many positive and negative reviews are there?
    100 xp
    Longest and shortest reviews
    100 xp
    Sentiment analysis types and approaches
    50 xp
    Detecting the sentiment of Tale of Two Cities
    100 xp
    Comparing the sentiment of two strings
    100 xp
    What is the sentiment of a movie review?
    100 xp
    Let's build a word cloud!
    50 xp
    Your first word cloud
    100 xp
    Which words are in the word cloud?
    50 xp
    Word Cloud on movie reviews
    100 xp
  2. 2

    Numeric Features from Reviews

    Imagine you are in the shoes of a company offering a variety of products. You want to know which of your products are bestsellers and most of all - why. We embark on step 1 of understanding the reviews of products, using a dataset with Amazon product reviews. To that end, we transform the text into a numeric form and consider a few complexities in the process.

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In the following tracks

Natural Language Processing

Collaborators

Ruanne Van Der WaltChester IsmayHillary Green-Lerman
Violeta Misheva Headshot

Violeta Misheva

Data Scientist

Violeta is a data scientist passionate about machine learning, natural language processing and fair and explainable algorithms, among others. She supplements her machine learning knowledge with her doctorate in applied econometrics and likes working on complex problems that require multi-disciplinary expertise. She regularly presents projects and initiatives she has worked on at conferences and is an advocate for diversity in the tech industry.
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