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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

Character count of Russian tweets

In this exercise, you have been given a dataframe tweets which contains some tweets associated with Russia's Internet Research Agency and compiled by FiveThirtyEight.

Your task is to create a new feature 'char_count' in tweets which computes the number of characters for each tweet. Also, compute the average length of each tweet. The tweets are available in the content feature of tweets.

Be aware that this is real data from Twitter and as such there is always a risk that it may contain profanity or other offensive content (in this exercise, and any following exercises that also use real Twitter data).

Instructions

100 XP
  • Create a new feature char_count by applying len to the 'content' feature of tweets.
  • Print the average character count of the tweets by computing the mean of the 'char_count' feature.