Frequentieanalyse van productreviews

Je hebt nu toegang tot een grotere gegevensset met TechZone-productreviews. Net als eerder heb je de reviews voorbewerkt en omgezet naar een BoW-representatie X. Jouw taak is nu om de woordfrequenties te analyseren en de meest voorkomende termen in de gegevensset te identificeren.

Om te helpen bij de analyse is een hulpfunctie get_top_ten() beschikbaar. Die neemt een lijst met woorden en hun bijbehorende aantallen en geeft de 10 meest frequente woorden met hun tellingen terug.

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Natural Language Processing (NLP) in Python

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def preprocess(text):
    text = text.lower()
    tokens = word_tokenize(text)
    tokens = [word for word in tokens if word not in string.punctuation]
    return " ".join(tokens)
  
cleaned_reviews = [preprocess(review) for review in product_reviews]
X = vectorizer.fit_transform(cleaned_reviews)

# Get word counts
word_counts = np.____(X.____, axis=0)
# Get words
words = vectorizer.____

top_words_with_stopwords, top_counts_with_stopwords = get_top_ten(words, word_counts)
print(top_words_with_stopwords, top_counts_with_stopwords)

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Deze oefening maakt deel uit van de cursus

Natural Language Processing (NLP) in Python

SkillTag.level.intermediateSkillTag.label

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 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

Huidige oefening

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