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Text Preprocessing with Python

Solution Explanations: Text Feature Engineering

Explore solutions for text feature engineering including how to construct text length features, apply TF-IDF and scaling techniques, and extract date components. Understand practical steps for transforming text data to improve NLP analysis and modeling.

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Solution 1: Feature construction

Here’s the solution:

Python 3.8
import pandas as pd
feedback_df = pd.read_csv('feedback.csv')
feedback_df['feedback_length_chars'] = feedback_df['feedback'].apply(lambda x: len(x))
feedback_df['feedback_length_words'] = feedback_df['feedback'].apply(lambda x: len(x.split()))
print(feedback_df['feedback_length_chars'])
print(feedback_df['feedback_length_words'])

Let’s go through the solution explanation:

  • Line 4: We create a new column named feedback_length_chars using the apply() function on the feedback column. For each text in the feedback column, we apply a lambda function that calculates the length of the text using len() and stores ...