TF-IDF representation of product feedback

You're working with a customer support team at a smart home company. They've collected user feedback on a range of smart devices and want to identify which words stand out in each review. You suggest using the TF-IDF technique to highlight the most relevant terms across feedback entries. Let's help them get started!

A preprocess() function that receives a text and returns a processed one is pre-loaded for you. This function applies lowercasing, tokenization, and punctuation removal. Pandas has been imported as pd, and the TfidfVectorizer class is ready to use.

Latihan ini adalah bagian dari kursus

Natural Language Processing (NLP) in Python

Petunjuk latihan

Initialize a TF-IDF vectorizer.
Transform the cleaned reviews into a tfidf_matrix.
Create a DataFrame df for the tfidf_matrix, having the vocabulary words as columns.

Latihan interaktif praktis

Cobalah latihan ini dengan menyelesaikan kode contoh berikut.

reviews = ["The smart speaker is incredible. Clear sound and fast responses!",
           "I am disappointed with the smart bulb. It stopped working in a week.",
           "The thermostat is okay. Not too smart, but functional."]
cleaned_reviews = [preprocess(review) for review in reviews]

# Initialize the vectorizer
vectorizer = ____
# Transform the cleaned reviews
tfidf_matrix = ____
# Create a DataFrame for TF-IDF
df = pd.DataFrame(
  tfidf_matrix.toarray(),
  columns=vectorizer.____
)
print(df.head())

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: Bag-of-Words representation Exercise 2: Building vocabulary from customer reviews Exercise 3: Transforming text to numbers with BoW Exercise 4: Frequency analysis of product reviews Exercise 5: Visualizing word frequencies Exercise 6: TF-IDF vectorization Exercise 7: TF-IDF representation of product feedback

Latihan Saat Ini

Exercise 8: Comparing BoW and TF-IDF representations Exercise 9: Embeddings Exercise 10: Exploring word relationships with embeddings Exercise 11: Visualizing and comparing word embeddings

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