Notice: This page requires JavaScript to function properly.
Please enable JavaScript in your browser settings or update your browser.Stopwords | Natural Language Handling
Natural Language Handling
course content

Course Content

Natural Language Handling

Natural Language Handling

IntroductionImport NLTKTokenizationStopwordsList ComprehensionStemmingTaggingLemmatizerRegexp TokenizerData VisualizationWord Cloud

bookStopwords

Stopwords are common words in a language that do not carry much meaning, such as "the", "and", and "of". In natural language processing tasks, removing stopwords is a common preprocessing step. This is because eliminating these words can improve the accuracy and efficiency of various algorithms and techniques applied to text data.

NLTK provides a built-in set of stopwords for several languages, including English, French, German, and Spanish. These stopwords can be easily removed from text using NLTK's stopwords module. By doing this, the resulting text data is left with only the most meaningful words, which can significantly enhance the performance of algorithms used in tasks like sentiment analysis and topic modeling.

Task

  1. Import the 'stopwords' corpus from NLTK.
  2. Create a set of English stopwords.
  3. Filter out stopwords from a tokenized text and create a list of non-stopword words.

Mark tasks as Completed
Switch to desktopSwitch to desktop for real-world practiceContinue from where you are using one of the options below
Everything was clear?

How can we improve it?

Thanks for your feedback!

Stopwords are common words in a language that do not carry much meaning, such as "the", "and", and "of". In natural language processing tasks, removing stopwords is a common preprocessing step. This is because eliminating these words can improve the accuracy and efficiency of various algorithms and techniques applied to text data.

NLTK provides a built-in set of stopwords for several languages, including English, French, German, and Spanish. These stopwords can be easily removed from text using NLTK's stopwords module. By doing this, the resulting text data is left with only the most meaningful words, which can significantly enhance the performance of algorithms used in tasks like sentiment analysis and topic modeling.

Task

  1. Import the 'stopwords' corpus from NLTK.
  2. Create a set of English stopwords.
  3. Filter out stopwords from a tokenized text and create a list of non-stopword words.

Mark tasks as Completed
Switch to desktopSwitch to desktop for real-world practiceContinue from where you are using one of the options below
Section 1. Chapter 4
AVAILABLE TO ULTIMATE ONLY
some-alt