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Removing Stop Words | Text Preprocessing Fundamentals
Introduction to NLP
course content

Contenido del Curso

Introduction to NLP

Introduction to NLP

1. Text Preprocessing Fundamentals
2. Stemming and Lemmatization
3. Basic Text Models
4. Word Embeddings

bookRemoving Stop Words

Understanding Stop Words

In NLP, the process of removing stop words is a crucial step in text preprocessing.

Stop words are typically filtered out after tokenization for NLP tasks, such as sentiment analysis, topic modeling, or keyword extraction. The rationale behind removing stop words is to decrease the dataset size, thereby improving computational efficiency, and to increase the relevance of the analysis by focusing on the words that carry significant meaning.

Removing Stop Words with NLTK

To make things easier, nltk provides a comprehensive list of stop words in multiple languages, which can be easily accessed and used to filter stop words from text data.

Here’s how you can get the list of English stop words in NLTK and convert it to set:

1234567
import nltk from nltk.corpus import stopwords # Download the stop words list nltk.download('stopwords') # Load English stop words stop_words = set(stopwords.words('english')) print(stop_words)
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With this in mind, let's take a look at a complete example of how to filter out stop words from a given text:

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import nltk from nltk.tokenize import word_tokenize from nltk.corpus import stopwords nltk.download('punkt_tab') nltk.download('stopwords') stop_words = set(stopwords.words('english')) text = "This is an example sentence demonstrating the removal of stop words." text = text.lower() # Tokenize the text tokens = word_tokenize(text) # Remove stop words filtered_tokens = [word for word in tokens if word.lower() not in stop_words] print("Original Tokens:", tokens) print("Filtered Tokens:", filtered_tokens)
copy

As you can see, we should first download the stop words and perform tokenization. The next step is to use a list comprehension to create a list containing only tokens which are not stop words. The word.lower() in the if clause is essential to convert each word (token) to lower case, since nltk contains stop words exclusively in lower case.

Tarea

Your task is to convert the text to lowercase, load the English stop words list from nltk and convert it to a set, then tokenize the text string using the word_tokenize() function, and filter out the stop words from tokens using list comprehension.

Switch to desktopCambia al escritorio para practicar en el mundo realContinúe desde donde se encuentra utilizando una de las siguientes opciones
¿Todo estuvo claro?

¿Cómo podemos mejorarlo?

¡Gracias por tus comentarios!

Sección 1. Capítulo 7
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bookRemoving Stop Words

Understanding Stop Words

In NLP, the process of removing stop words is a crucial step in text preprocessing.

Stop words are typically filtered out after tokenization for NLP tasks, such as sentiment analysis, topic modeling, or keyword extraction. The rationale behind removing stop words is to decrease the dataset size, thereby improving computational efficiency, and to increase the relevance of the analysis by focusing on the words that carry significant meaning.

Removing Stop Words with NLTK

To make things easier, nltk provides a comprehensive list of stop words in multiple languages, which can be easily accessed and used to filter stop words from text data.

Here’s how you can get the list of English stop words in NLTK and convert it to set:

1234567
import nltk from nltk.corpus import stopwords # Download the stop words list nltk.download('stopwords') # Load English stop words stop_words = set(stopwords.words('english')) print(stop_words)
copy

With this in mind, let's take a look at a complete example of how to filter out stop words from a given text:

1234567891011121314
import nltk from nltk.tokenize import word_tokenize from nltk.corpus import stopwords nltk.download('punkt_tab') nltk.download('stopwords') stop_words = set(stopwords.words('english')) text = "This is an example sentence demonstrating the removal of stop words." text = text.lower() # Tokenize the text tokens = word_tokenize(text) # Remove stop words filtered_tokens = [word for word in tokens if word.lower() not in stop_words] print("Original Tokens:", tokens) print("Filtered Tokens:", filtered_tokens)
copy

As you can see, we should first download the stop words and perform tokenization. The next step is to use a list comprehension to create a list containing only tokens which are not stop words. The word.lower() in the if clause is essential to convert each word (token) to lower case, since nltk contains stop words exclusively in lower case.

Tarea

Your task is to convert the text to lowercase, load the English stop words list from nltk and convert it to a set, then tokenize the text string using the word_tokenize() function, and filter out the stop words from tokens using list comprehension.

Switch to desktopCambia al escritorio para practicar en el mundo realContinúe desde donde se encuentra utilizando una de las siguientes opciones
¿Todo estuvo claro?

¿Cómo podemos mejorarlo?

¡Gracias por tus comentarios!

Sección 1. Capítulo 7
toggle bottom row

bookRemoving Stop Words

Understanding Stop Words

In NLP, the process of removing stop words is a crucial step in text preprocessing.

Stop words are typically filtered out after tokenization for NLP tasks, such as sentiment analysis, topic modeling, or keyword extraction. The rationale behind removing stop words is to decrease the dataset size, thereby improving computational efficiency, and to increase the relevance of the analysis by focusing on the words that carry significant meaning.

Removing Stop Words with NLTK

To make things easier, nltk provides a comprehensive list of stop words in multiple languages, which can be easily accessed and used to filter stop words from text data.

Here’s how you can get the list of English stop words in NLTK and convert it to set:

1234567
import nltk from nltk.corpus import stopwords # Download the stop words list nltk.download('stopwords') # Load English stop words stop_words = set(stopwords.words('english')) print(stop_words)
copy

With this in mind, let's take a look at a complete example of how to filter out stop words from a given text:

1234567891011121314
import nltk from nltk.tokenize import word_tokenize from nltk.corpus import stopwords nltk.download('punkt_tab') nltk.download('stopwords') stop_words = set(stopwords.words('english')) text = "This is an example sentence demonstrating the removal of stop words." text = text.lower() # Tokenize the text tokens = word_tokenize(text) # Remove stop words filtered_tokens = [word for word in tokens if word.lower() not in stop_words] print("Original Tokens:", tokens) print("Filtered Tokens:", filtered_tokens)
copy

As you can see, we should first download the stop words and perform tokenization. The next step is to use a list comprehension to create a list containing only tokens which are not stop words. The word.lower() in the if clause is essential to convert each word (token) to lower case, since nltk contains stop words exclusively in lower case.

Tarea

Your task is to convert the text to lowercase, load the English stop words list from nltk and convert it to a set, then tokenize the text string using the word_tokenize() function, and filter out the stop words from tokens using list comprehension.

Switch to desktopCambia al escritorio para practicar en el mundo realContinúe desde donde se encuentra utilizando una de las siguientes opciones
¿Todo estuvo claro?

¿Cómo podemos mejorarlo?

¡Gracias por tus comentarios!

Understanding Stop Words

In NLP, the process of removing stop words is a crucial step in text preprocessing.

Stop words are typically filtered out after tokenization for NLP tasks, such as sentiment analysis, topic modeling, or keyword extraction. The rationale behind removing stop words is to decrease the dataset size, thereby improving computational efficiency, and to increase the relevance of the analysis by focusing on the words that carry significant meaning.

Removing Stop Words with NLTK

To make things easier, nltk provides a comprehensive list of stop words in multiple languages, which can be easily accessed and used to filter stop words from text data.

Here’s how you can get the list of English stop words in NLTK and convert it to set:

1234567
import nltk from nltk.corpus import stopwords # Download the stop words list nltk.download('stopwords') # Load English stop words stop_words = set(stopwords.words('english')) print(stop_words)
copy

With this in mind, let's take a look at a complete example of how to filter out stop words from a given text:

1234567891011121314
import nltk from nltk.tokenize import word_tokenize from nltk.corpus import stopwords nltk.download('punkt_tab') nltk.download('stopwords') stop_words = set(stopwords.words('english')) text = "This is an example sentence demonstrating the removal of stop words." text = text.lower() # Tokenize the text tokens = word_tokenize(text) # Remove stop words filtered_tokens = [word for word in tokens if word.lower() not in stop_words] print("Original Tokens:", tokens) print("Filtered Tokens:", filtered_tokens)
copy

As you can see, we should first download the stop words and perform tokenization. The next step is to use a list comprehension to create a list containing only tokens which are not stop words. The word.lower() in the if clause is essential to convert each word (token) to lower case, since nltk contains stop words exclusively in lower case.

Tarea

Your task is to convert the text to lowercase, load the English stop words list from nltk and convert it to a set, then tokenize the text string using the word_tokenize() function, and filter out the stop words from tokens using list comprehension.

Switch to desktopCambia al escritorio para practicar en el mundo realContinúe desde donde se encuentra utilizando una de las siguientes opciones
Sección 1. Capítulo 7
Switch to desktopCambia al escritorio para practicar en el mundo realContinúe desde donde se encuentra utilizando una de las siguientes opciones
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