Natural Language Processing Recipes: Unlocking Text Data with Machine Learning and Deep Learning using Python

© Akshay Kulkarni and Adarsha Shivananda 2019

Akshay Kulkarni and Adarsha ShivanandaNatural Language Processing Recipeshttps://doi.org/10.1007/978-1-4842-4267-4_3

3. Converting Text to Features

Akshay Kulkarni¹  and Adarsha Shivananda¹

(1)

Bangalore, Karnataka, India

 

In this chapter, we are going to cover basic to advanced feature engineering (text to features) methods. By the end of this chapter, you will be comfortable with the following recipes:

• Recipe 1. One Hot encoding

• Recipe 2. Count vectorizer

• Recipe 3. N-grams

• Recipe 4. Co-occurrence matrix

• Recipe 5. Hash vectorizer

• Recipe 6. Term Frequency-Inverse Document Frequency (TF-IDF)

• Recipe 7. Word embedding

• Recipe 8. Implementing fastText

Now that all the text preprocessing steps are discussed, let’s ...