TF-IDF-representatie van productfeedback

Je werkt samen met een klantenserviceteam bij een smart home-bedrijf. Ze hebben gebruikersfeedback verzameld over verschillende slimme apparaten en willen weten welke woorden in elke review eruit springen. Jij stelt voor om de TF-IDF-techniek te gebruiken om de meest relevante termen in de feedback te markeren. Laten we ze op weg helpen!

Een functie preprocess() die tekst ontvangt en een bewerkte versie teruggeeft, is alvast voor je geladen. Deze functie past lowercasing, tokenization en het verwijderen van leestekens toe. Pandas is geïmporteerd als pd, en de klasse TfidfVectorizer is klaar voor gebruik.

Deze oefening maakt deel uit van de cursus

Natural Language Processing (NLP) in Python

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Oefeninstructies

Initialiseer een TF-IDF-vectorizer.
Zet de opgeschoonde reviews om in een tfidf_matrix.
Maak een DataFrame df voor de tfidf_matrix, met de woorden uit de woordenschat als kolommen.

Praktische interactieve oefening

Probeer deze oefening eens door deze voorbeeldcode in te vullen.

reviews = ["The smart speaker is incredible. Clear sound and fast responses!",
           "I am disappointed with the smart bulb. It stopped working in a week.",
           "The thermostat is okay. Not too smart, but functional."]
cleaned_reviews = [preprocess(review) for review in reviews]

# Initialize the vectorizer
vectorizer = ____
# Transform the cleaned reviews
tfidf_matrix = ____
# Create a DataFrame for TF-IDF
df = pd.DataFrame(
  tfidf_matrix.toarray(),
  columns=vectorizer.____
)
print(df.head())

Code bewerken en uitvoeren

Deze oefening maakt deel uit van de cursus

Natural Language Processing (NLP) in Python

SkillTag.level.intermediateSkillTag.label

4.8+

Begin de cursus gratis

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 Exercise 9: Stemming Exercise 10: Lemmatization

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-representatie Exercise 2: Woordenschat opbouwen uit klantreviews Exercise 3: Tekst omzetten naar getallen met BoW Exercise 4: Frequentieanalyse van productreviews Exercise 5: Woordfrequenties visualiseren Exercise 6: TF-IDF-vectorisatie Exercise 7: TF-IDF-representatie van productfeedback

Huidige oefening

Exercise 8: BoW- en TF-IDF-representaties vergelijken Exercise 9: Embeddings Exercise 10: Woordrelaties verkennen met embeddings Exercise 11: Woordembeddings visualiseren en vergelijken

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