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  3. Feature Engineering for NLP in Python
  • 1

    Basic features and readability scores

    Free

    Learn to compute basic features such as number of words, number of characters, average word length and number of special characters (such as Twitter hashtags and mentions). You will also learn to compute readability scores and determine the amount of education required to comprehend a piece of text.

  • 2

    Text preprocessing, POS tagging and NER

    In this chapter, you will learn about tokenization and lemmatization. You will then learn how to perform text cleaning, part-of-speech tagging, and named entity recognition using the spaCy library. Upon mastering these concepts, you will proceed to make the Gettysburg address machine-friendly, analyze noun usage in fake news, and identify people mentioned in a TechCrunch article.

  • 3

    N-Gram models

    Learn about n-gram modeling and use it to perform sentiment analysis on movie reviews.

  • 4

    TF-IDF and similarity scores

    Learn how to compute tf-idf weights and the cosine similarity score between two vectors. You will use these concepts to build a movie and a TED Talk recommender. Finally, you will also learn about word embeddings and using word vector representations, you will compute similarities between various Pink Floyd songs.

Initializing

Exercise

Readability of 'The Myth of Sisyphus'

In this exercise, you will compute the Flesch reading ease score for Albert Camus' famous essay The Myth of Sisyphus. We will then interpret the value of this score as explained in the video and try to determine the reading level of the essay.

The entire essay is in the form of a string and is available as sisyphus_essay.

Instructions

100 XP
  • Import the Readability class from readability.
  • Compute the readability_scores object for sisyphus_essay using Readability.
  • Print the Flesch reading ease score using the flesch method.