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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

Cleaning TED talks in a dataframe

In this exercise, we will revisit the TED Talks from the first chapter. You have been a given a dataframe ted consisting of 5 TED Talks. Your task is to clean these talks using techniques discussed earlier by writing a function preprocess and applying it to the transcript feature of the dataframe.

The stopwords list is available as stopwords.

Instructions

100 XP
  • Generate the Doc object for text. Ignore the disable argument for now.
  • Generate lemmas using list comprehension using the lemma_ attribute.
  • Remove non-alphabetic characters using isalpha() in the if condition.