1. Learn
    2. /
  2. Courses
    3. /
  3. Sentiment Analysis in Python
  • 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!

Initializing

Exercise

Tokenize a string from GoT

A first standard step when working with text is to tokenize it, in other words, split a bigger string into individual strings, which are usually single words (tokens).

A string GoT has been created for you and it contains a quote from George R.R. Martin's Game of Thrones. Your task is to split it into individual tokens.

Instructions

100 XP
  • Import the word tokenizing function from nltk.
  • Transform the GoT string to word tokens.