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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

Identifying people mentioned in a news article

In this exercise, you have been given an excerpt from a news article published in TechCrunch. Your task is to write a function find_people that identifies the names of people that have been mentioned in a particular piece of text. You will then use find_people to identify the people of interest in the article.

The article is available as the string tc and has been printed to the console. The required spacy model has also been already loaded as nlp.

Instructions

100 XP
  • Create a Doc object for text.
  • Using list comprehension, loop through doc.ents and create a list of named entities whose label is PERSON.
  • Using find_persons(), print the people mentioned in tc.