Stopwords are common words in a language that do not carry much meaning, such as "the", "and" and "of". Removing stopwords is a common preprocessing step in natural language processing tasks, as it can help to improve the accuracy and efficiency of 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 a text using NLTK's `stopwords` module. By removing stopwords, the resulting text data contains only the most meaningful words, which can improve the accuracy of algorithms such as sentiment analysis and topic modeling.

This project will use the NLTK for tokenization, stemming, tagging, parsing, and machine learning for text data.