Membandingkan representasi BoW dan TF-IDF

Anda adalah bagian dari tim analitik di sebuah perusahaan perangkat wearable. Tujuan Anda adalah membantu manajer produk memahami umpan balik pelanggan atas smartwatch baru perusahaan. Anda telah melakukan prapemrosesan teks dan membuat dua representasi: bow_matrix menggunakan CountVectorizer(), dan tfidf_matrix menggunakan TfidfVectorizer(). Dalam latihan ini, Anda akan memvisualisasikan dan membandingkan keduanya untuk memahami lebih baik bagaimana masing-masing menangkap pentingnya kata.

Latihan ini adalah bagian dari kursus

Natural Language Processing (NLP) in Python

Latihan interaktif praktis

Cobalah latihan ini dengan menyelesaikan kode contoh berikut.

# Convert BoW matrix to a DataFrame
df_bow = pd.DataFrame(
    ____,
    columns=vectorizer.____
)

# Plot the heatmap
plt.figure(figsize=(10, 6))
sns.heatmap(____, annot=True)
plt.title("BoW Scores Across Reviews")
plt.xlabel("Terms")
plt.xticks(rotation=45)
plt.ylabel("Documents")
plt.show()

Edit dan Jalankan Kode

Latihan ini adalah bagian dari kursus

Natural Language Processing (NLP) in Python

SkillTag.level.intermediateSkillTag.label

4.8+

Mulai Kursus 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: Representasi Bag-of-Words Exercise 2: Membangun kosakata dari ulasan pelanggan Exercise 3: Mengonversi teks menjadi angka dengan BoW Exercise 4: Analisis frekuensi ulasan produk Exercise 5: Memvisualisasikan frekuensi kata Exercise 6: Vektorisasi TF-IDF Exercise 7: Representasi TF-IDF dari umpan balik produk Exercise 8: Membandingkan representasi BoW dan TF-IDF

Latihan Saat Ini

Exercise 9: Embeddings Exercise 10: Mengeksplorasi relasi kata dengan embeddings Exercise 11: Memvisualisasikan dan membandingkan word embedding

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