Interactive Course

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.

  • 4 hours
  • 16 Videos
  • 60 Exercises
  • 1,380 Participants
  • 5,050 XP

Loved by learners at thousands of top companies:

t-mobile-grey.svg
dell-grey.svg
siemens-grey.svg
uber-grey.svg
whole-foods-grey.svg
ea-grey.svg

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.

  2. More on Numeric Vectors: Transforming Tweets

    This chapter continues the process of understanding product reviews. We will cover additional complexities, especially when working with sentiment analysis data from social media platforms such as Twitter. We will also learn other ways to obtain numeric features from the text.

  3. 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.

  4. Let's Predict the Sentiment

    We employ machine learning to predict the sentiment of a review based on the words used in the review. We use logistic regression and evaluate its performance in a few different ways. These are some solid first models!

  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.

  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.

  3. More on Numeric Vectors: Transforming Tweets

    This chapter continues the process of understanding product reviews. We will cover additional complexities, especially when working with sentiment analysis data from social media platforms such as Twitter. We will also learn other ways to obtain numeric features from the text.

  4. Let's Predict the Sentiment

    We employ machine learning to predict the sentiment of a review based on the words used in the review. We use logistic regression and evaluate its performance in a few different ways. These are some solid first models!

What do other learners have to say?

Devon

“I've used other sites, but DataCamp's been the one that I've stuck with.”

Devon Edwards Joseph

Lloyd's Banking Group

Louis

“DataCamp is the top resource I recommend for learning data science.”

Louis Maiden

Harvard Business School

Ronbowers

“DataCamp is by far my favorite website to learn from.”

Ronald Bowers

Decision Science Analytics @ USAA

Violeta Misheva
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.

See More
Collaborators
  • Hillary Green-Lerman

    Hillary Green-Lerman

  • Chester Ismay

    Chester Ismay

  • Ruanne Van Der Walt

    Ruanne Van Der Walt

Icon Icon Icon professional info