Woordenschat opbouwen uit klantreviews

Je maakt deel uit van een productanalyseteam bij TechZone, een consumentenelektronicabedrijf. Je hebt een kleine batch klantreviews ontvangen voor een nieuwe gadget. Om de reviews te analyseren, ga je eerst de tekst voorbewerken en een woordenschat opbouwen: een lijst met unieke woorden die de features definieert waarmee je elke review als numerieke data kunt representeren.

Een preprocess()-functie is alvast voor je geladen. Deze zet de tekst om naar kleine letters, tokeniseert, en verwijdert interpunctie.

Deze oefening maakt deel uit van de cursus

Natural Language Processing (NLP) in Python

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Oefeninstructies

Voorbewerk elke review in de gegevensset met de functie preprocess().
Train de vectorizer op de voorbewerkte reviews.
Print de resulterende woordenschat.

Praktische interactieve oefening

Probeer deze oefening eens door deze voorbeeldcode in te vullen.

reviews = [
    "The product is fantastic! It works like a charm.",
    "I hated the product. It broke after one use.",
    "Product was okay, not the best, but fine overall."
]
# Preprocess the reviews
cleaned_reviews = [____ for ____ in ____]

vectorizer = CountVectorizer()
# Fit the vectorizer
vectorizer.____
# Print the vocabulary 
print(vectorizer.____)

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

Huidige oefening

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 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