Text Preprocessing with Python/

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Best Practices

Best Practices

Learn about the best practices for handling irrelevant text data.

We'll cover the following...

Robust data preprocessing

In this lesson, we’ll cover some best practices to adopt when dealing with irrelevant text data. We’ll start by covering robust data preprocessing, which involves handling irrelevant text data by cleaning and transforming the data into a format that can be effectively analyzed. This might mean undertaking several steps, such as tokenization, stopword removal, stemming or lemmatization, and noise removal from the text. Here’s a code example that explores robust data preprocessing using NLTK:

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import nltk
from nltk.corpus import stopwords
from nltk.tokenize import word_tokenize, RegexpTokenizer
import re
nltk.download('punkt', quiet=True)
nltk.download('stopwords', quiet=True)
def preprocess_text(text):
    text = text.lower()
    tokenizer = RegexpTokenizer(r'\w+')
    tokens = tokenizer.tokenize(text)
    stop_words = set(stopwords.words('english'))
    tokens_without_stopwords = [token for token in tokens if token.lower() not in stop_words]
    combined_text = ' '.join(tokens_without_stopwords) 
    processed_text = re.sub(r'[^\w\s]', '', combined_text)
    return processed_text
text = "I'll be going to the park, and we're meeting at 3 o'clock. It's a beautiful day!"
processed_text = preprocess_text(text)
print(processed_text)

Let’s review the code line by line:

  • Lines 1–6: We import the necessary modules and download the required NLTK resources for text processing.

  • Lines 8–16: We define the preprocess_text function that takes a text as input and performs various preprocessing steps on it:

    • We convert ...

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