Text mit BoW in Zahlen umwandeln

Nachdem du aus den Kundenrezensionen ein Vokabular erstellt hast, kannst du nun jede Rezension mit dem Bag-of-Words-(BoW)-Modell in ein numerisches Format umwandeln. Dieser Schritt erzeugt eine strukturierte Matrix, in der jede Zeile eine Rezension darstellt und jede Spalte einem Wort aus dem Vokabular entspricht.

Die Liste cleaned_reviews und der angepasste vectorizer sind bereits für dich geladen.

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<Kurs>Natural Language Processing (NLP) in Python</Kurs>

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Übungsanweisungen

Wandle die cleaned_reviews in eine bow_matrix um.
Gib die BoW-Darstellung als NumPy-Array aus.

Interaktive praktische Übung

Versuche dich an dieser Übung, indem du diesen Beispielcode vervollständigst.

# Transform the reviews
bow_matrix = vectorizer.____(____)

# Print the BoW representation
print(____.____())

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Diese Übung ist Teil des Kurses

<Kurs>Natural Language Processing (NLP) in Python</Kurs>

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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 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-Darstellung Exercise 2: Wortschatz aus Kundenrezensionen aufbauen Exercise 3: Text mit BoW in Zahlen umwandeln

Aktuelle Übung

Exercise 4: Häufigkeitsanalyse von Produktbewertungen Exercise 5: Worthäufigkeiten visualisieren Exercise 6: TF-IDF-Vektorisierung Exercise 7: TF-IDF-Darstellung von Produktfeedback Exercise 8: BoW- und TF-IDF-Repräsentationen vergleichen Exercise 9: Embeddings Exercise 10: Wortbeziehungen mit Embeddings erkunden Exercise 11: Wort-Embeddings visualisieren und vergleichen

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