Text Preprocessing with Python/

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Solution Explanations: N-Grams

Solution Explanations: N-Grams

Review solution explanations for the code challenges on n-grams.

We'll cover the following...

Solution 1: Introduction to n-grams

Here’s the solution:

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main.py
feedback.csv
import pandas as pd
from sklearn.feature_extraction.text import CountVectorizer
from nltk.tokenize import word_tokenize
import string
feedback_df = pd.read_csv('feedback.csv')
def preprocess(text):
    text = text.lower()
    translator = str.maketrans('', '', string.punctuation)
    text = text.translate(translator)
    return text
feedback_df['feedback'] = feedback_df['feedback'].apply(preprocess)
vectorizer = CountVectorizer(tokenizer=word_tokenize, ngram_range=(2, 3))
X = vectorizer.fit_transform(feedback_df['feedback'])
grams = vectorizer.get_feature_names()
print(grams)

Let’s go through the solution explanation:

  • Lines 7–11: We define the preprocess() function that lowercases text and removes its punctuation characters.

  • Line 12: We then apply the preprocess() function to the feedback column using the apply() method and save the result in the feedback column. ...