Conversione in minuscolo

Stai analizzando le recensioni degli utenti per un sito di viaggi. Queste recensioni spesso includono maiuscole incoerenti come "TRAVEL" e "travel". Per preparare il testo all'analisi del sentiment e all'estrazione degli argomenti, per prima cosa convertirai tutte le parole in minuscolo, poi le tokenizzerai e le pulirai rimuovendo stop word e punteggiatura.

Sono stati forniti la funzione word_tokenize() e un elenco stop_words. Le risorse NLTK sono già state scaricate.

Questo esercizio fa parte del corso

Natural Language Processing (NLP) in Python

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Istruzioni dell'esercizio

Converti in minuscolo la review fornita.
Tokenizza lower_text in parole.
Usa una list comprehension per rimuovere stop word e punteggiatura utilizzando le liste stop_words e string.punctuation.

Esercizio pratico interattivo

Prova a risolvere questo esercizio completando il codice di esempio.

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)

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Questo esercizio fa parte del corso

Natural Language Processing (NLP) in Python

IntermediárioNível de habilidade

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: Introduzione all'elaborazione del linguaggio naturale Exercise 2: Tokenizzazione di frasi e parole Exercise 3: Flusso di lavoro NLP Exercise 4: Gestione delle stop word e della punteggiatura Exercise 5: Rimozione delle stop words Exercise 6: Rimozione della punteggiatura Exercise 7: Tecniche di normalizzazione del testo Exercise 8: Conversione in minuscolo

Esercizio in corso

Exercise 9: Stemming Exercise 10: Lemmatizzazione

Transform raw text into powerful numerical features. Create Bag-of-Words and TF-IDF representations to capture word importance across documents, then explore word embeddings like Word2Vec and GloVe to uncover deep semantic patterns. Visualize frequency, relevance, and similarity to bring your text data to life.

Exercise 1: Bag-of-Words representation Exercise 2: Building vocabulary from customer reviews Exercise 3: Transforming text to numbers with BoW Exercise 4: Frequency analysis of product reviews Exercise 5: Visualizing word frequencies Exercise 6: TF-IDF vectorization Exercise 7: TF-IDF representation of product feedback Exercise 8: Comparing BoW and TF-IDF representations Exercise 9: Embeddings Exercise 10: Exploring word relationships with embeddings Exercise 11: Visualizing and comparing word embeddings

Harness the power of pre-trained models to perform advanced text classification tasks. Use Hugging Face pipelines for sentiment analysis, topic classification, and natural language inference. Evaluate semantic similarity and grammatical correctness with state-of-the-art models, all without building anything from scratch.

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

Dive into the core of modern NLP applications with token classification and text generation techniques. Learn to extract meaningful entities and grammatical structures using NER and PoS tagging. Master both extractive and abstractive question answering, and explore advanced generation tasks including summarization, translation, and language modeling using Hugging Face pipelines.

Exercise 1: Token classification Exercise 2: Identifying named entities in news headlines Exercise 3: Part of Speech tagging for text analysis Exercise 4: Question answering Exercise 5: Answering questions from product descriptions Exercise 6: Generating natural answers with abstractive QA Exercise 7: Sequence generation tasks Exercise 8: Summarizing news articles for quick insights Exercise 9: Translating customer reviews to French Exercise 10: Building a search completion system Exercise 11: Congratulations