Lowercasing

You're analyzing user reviews for a travel website. These reviews often include inconsistent capitalization like "TRAVEL" and "travel". To prepare the text for sentiment analysis and topic extraction, you'll first convert all words to lowercase, then tokenize them and clean them from stop words and punctuation.

The word_tokenize() function, a stop_words list have been provided. NLTK resources are already downloaded.

This exercise is part of the course

Natural Language Processing (NLP) in Python

Exercise instructions

Convert the provided review into lowercase.
Tokenize the lower_text into words.
Use list comprehension to remove stop words and punctuation using the lists of stop_words and string.punctuation.

Hands-on interactive exercise

Have a go at this exercise by completing this sample code.

review = "I have been FLYING a lot lately and the Flights just keep getting DELAYED. Honestly, traveling for WORK gets exhausting with endless delays, but every trip teaches you something new!"

# Lowercase the review
lower_text = ____

# Tokenize the lower_text into words
tokens = ____

# Remove stop words and punctuation
clean_tokens = [____ if word ____ and word ____]

print(clean_tokens)

Edit and Run Code

This exercise is part of the course

Natural Language Processing (NLP) in Python

IntermediateSkill Level

4.8+

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Learn the essentials of text processing in Natural Language Processing (NLP). Master techniques such as tokenization, stop word and punctuation removal, and text normalization with lowercasing, stemming, and lemmatization to prepare text data for further analysis and insight extraction.

Exercise 1: Introduction to natural language processing Exercise 2: Sentence and word tokenization Exercise 3: NLP workflow Exercise 4: Stop words and punctuation handling Exercise 5: Removing stop words Exercise 6: Removing punctuation Exercise 7: Text normalization techniques Exercise 8: Lowercasing

Current Exercise

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Exercise 1: Hugging Face pipelines for sentiment analysis Exercise 2: Analyzing the sentiment of a review Exercise 3: Batch classifying multiple reviews Exercise 4: Comparing models on labeled review data Exercise 5: Zero-shot classification and QNLI Exercise 6: Zero-shot classification of support tickets Exercise 7: Does the text answer the question?Exercise 8: Question similarity and grammatical correctness Exercise 9: Detecting duplicate questions Exercise 10: Checking grammatical correctness

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