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Text Preprocessing with Python

Solution Explanations: Advanced Text Preprocessing

Understand advanced text preprocessing methods such as part-of-speech tagging, named entity recognition, and text classification. Learn to implement these with Python libraries, improving your ability to prepare and analyze text data for NLP applications.

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Solution 1: Part-of-speech tagging

Here’s the solution:

Python 3.8
import pandas as pd
import nltk
import string
from nltk.tokenize import word_tokenize
from nltk.corpus import stopwords
nltk.download('averaged_perceptron_tagger', quiet=True) 
feedback_df = pd.read_csv('feedback.csv') 
feedback_df['tokens'] = feedback_df['feedback'].apply(lambda text: word_tokenize(text.lower()))
stop_words = set(stopwords.words('english'))
feedback_df['tokens'] = feedback_df['tokens'].apply(lambda tokens: [token for token in tokens if token not in stop_words])
feedback_df['tokens'] = feedback_df['tokens'].apply(lambda tokens: [token for token in tokens if token not in string.punctuation])
feedback_df['pos_tags'] = feedback_df['tokens'].apply(nltk.pos_tag)
print(feedback_df['pos_tags'])

Let’s go through the solution explanation:

  • Line 9: We tokenize the text in the text column using the word_tokenize function and convert each token to lowercase. We then save the tokenized text as a new tokens column.

  • Line 10: We create a set of stopwords using the stopwords.words('english') function. ...