Visualizando frequências de palavras

Agora que você calculou as palavras mais frequentes com e sem stop words, é hora de visualizar as diferenças. Neste exercício, você vai usar o matplotlib para criar gráficos de barras para os dois casos.

As seguintes listas já foram carregadas para você: top_words_without_stopwords, top_counts_without_stopwords, top_words_with_stopwords, top_counts_with_stopwords.

Este exercicio faz parte do curso

Processamento de Linguagem Natural (NLP) em Python

Instruções do exercicio

Use plt.bar() para plotar as 10 palavras mais frequentes com stop words.
Use plt.bar() para plotar as 10 palavras mais frequentes sem stop words.

exercicio interativo prático

Tente este exercicio completando este código de exemplo.

import matplotlib.pyplot as plt
# Plot the frequencies with stop words
plt.bar(____, ____)
plt.title("Top 10 word frequencies (with stop words)")
plt.xlabel("Words")
plt.ylabel("Frequency")
plt.show()

# Plot the frequencies without stop words
plt.figure()
plt.bar(____, ____)
plt.title("Top 10 word frequencies (without stop words)")
plt.xlabel("Words")
plt.ylabel("Frequency")
plt.show()

Editar e Executar Código

Este exercicio faz parte do curso

Processamento de Linguagem Natural (NLP) em Python

IntermediárioNível de habilidade

4.9+

Comece o curso gratuitamente

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: Representação Bag-of-Words Exercise 2: Construindo vocabulário a partir de avaliações de clientes Exercise 3: Transformando texto em números com BoW Exercise 4: Análise de frequência em avaliações de produtos Exercise 5: Visualizando frequências de palavras

Exercicio Atual

Exercise 6: Vetorização TF-IDF Exercise 7: Representação TF-IDF do feedback de produtos Exercise 8: Comparando as representações BoW e TF-IDF Exercise 9: Embeddings Exercise 10: Explorando relações entre palavras com embeddings Exercise 11: Visualizando e comparando 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