202511191250 Status: idea Tags: Datascience, NLP

NLP Stopword Removal

• Stopwords : Common words (articles, prepositions, pronouns) with little semantic value.
• Examples : “the”, “and”, “is”, “in”.
• Importance : Proper handling improves NLP performance and efficiency

When do you remove stopwords?

Beneficial for:

• Text classification
• sentiment analysis
• Information retrieval
• search engines
• Topic modeling
• clustering
• Keyword extraction Keep stopwords for:
• Machine translation (grammar structure)
• Text summarization (sentence coherence)
• Question answering systems (syntactic relations)
• Grammar checking, parsing

Types of stopwords

• Standard stopwords : Function words (“a”, “the”, “in”, “on”)
• Domain-specific stopwords : Field-dependent terms (e.g., “patient” in medical texts)
• Contextual stopwords : Extremely frequent dataset-specific words
• Numerical stopwords : Digits, punctuation marks, single character

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References

• Dit is iets wat we leren voor Datascience. dit was informatie vanuit avans 2-2 datascience 2025-11-12. en daarbij horen deze slides
• I was writing a note about NLP which mentions this.