Basic feature extraction

Feature Engineering for NLP in Python

Rounak Banik

Data Scientist

Number of characters

"I don't know." # 13 characters

# Compute the number of characters 
text = "I don't know."
num_char = len(text)

# Print the number of characters
print(num_char)

# Create a 'num_chars' feature
df['num_chars'] = df['review'].apply(len)

Number of words

# Split the string into words
text = "Mary had a little lamb."
words = text.split()

# Print the list containing words
print(words)

['Mary', 'had', 'a', 'little', 'lamb.']

# Print number of words
print(len(words))

Number of words

# Function that returns number of words in string
def word_count(string):
    # Split the string into words
    words = string.split()

    # Return length of words list
    return len(words)

# Create num_words feature in df
df['num_words'] = df['review'].apply(word_count)

Average word length

#Function that returns average word length
def avg_word_length(x):

    # Split the string into words
    words = x.split()

    # Compute length of each word and store in a separate list
    word_lengths = [len(word) for word in words]

    # Compute average word length
    avg_word_length = sum(word_lengths)/len(words)

    # Return average word length
    return(avg_word_length)

Average word length

# Create a new feature avg_word_length
df['avg_word_length'] = df['review'].apply(doc_density)

Special features

Datacamp Tweet

Hashtags and mentions

# Function that returns number of hashtags
def hashtag_count(string):

    # Split the string into words
    words = string.split()

    # Create a list of hashtags
    hashtags = [word for word in words if word.startswith('#')]

    # Return number of hashtags
    return len(hashtags)

hashtag_count("@janedoe This is my first tweet! #FirstTweet #Happy")

Other features

Number of sentences
Number of paragraphs
Words starting with an uppercase
All-capital words
Numeric quantities

Let's practice!

Feature Engineering for NLP in Python