Ürün yorumlarının frekans analizi

Artık TechZone ürün yorumlarından oluşan daha büyük bir veri kümesine erişimin var. Daha önce olduğu gibi, yorumları ön işledin ve BoW temsiline X olarak dönüştürdün. Görevin şimdi kelime frekanslarını analiz etmek ve veri kümesindeki en yaygın terimleri belirlemek.

Analize yardımcı olmak için get_top_ten() adlı bir yardımcı fonksiyon sağlandı. Bu fonksiyon, bir kelime listesi ile bunlara karşılık gelen sayımları alır ve en sık geçen 10 kelimeyi ve sayımlarını döndürür.

Bu egzersiz

Python ile Natural Language Processing (NLP)

kursunun bir parçasıdır

Kursu Görüntüle

Uygulamalı interaktif egzersiz

Bu örnek kodu tamamlayarak bu egzersizi bitirin.

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)

Kodu Düzenle ve Çalıştır

Bu egzersiz

Python ile Natural Language Processing (NLP)

kursunun bir parçasıdır

IntermediárioNível de habilidade

4.8+

Kursa Ücretsiz Başlayın

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 gösterimi Exercise 2: Müşteri incelemelerinden sözlük oluşturma Exercise 3: BoW ile metni sayılara dönüştürme Exercise 4: Ürün yorumlarının frekans analizi

Geçerli Egzersiz

Exercise 5: Kelime frekanslarını görselleştirme Exercise 6: TF-IDF vektörleştirme Exercise 7: Ürün geri bildirimlerinin TF-IDF temsili Exercise 8: BoW ve TF-IDF gösterimlerini karşılaştırma Exercise 9: Gömlemeler Exercise 10: Gömme vektörlerle kelime ilişkilerini keşfetme Exercise 11: Kelime gömme vektörlerini görselleştirme ve karşılaştırma

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