Using TF-IDF and scikit-learn to Weight Document Keywords

Summary

This tutorial explains term frequency–inverse document frequency (TF-IDF), a method for assigning greater weight to words that occur often in one document but less often across a corpus. Term frequency captures within-document prevalence, while inverse document frequency reduces the influence of words common to many documents. Their combination is used to rank terms that may distinguish each text.

The article demonstrates scikit-learn’s TfidfVectorizer on two segmented Chinese text samples, including an optional stop-word list. It describes the output as a document-by-term matrix and shows how to inspect weights for each term in each document. The examples illustrate that a zero weight means a term is absent from that document, while larger values reflect the vectorizer’s weighting scheme. This is a general NLP feature extraction technique rather than a trading strategy; results depend on corpus composition, tokenization, stop-word choices, and vectorizer settings, and the tutorial reports no financial application or predictive evidence.

Key ideas

• TF-IDF combines within-document term frequency with inverse document frequency across a corpus.
• Terms common across many documents receive less distinguishing weight than rarer terms.
• TfidfVectorizer creates a document-by-term matrix that can be used to compare term weights.
• Stop-word handling and text segmentation affect the resulting vocabulary and weights.
• The example teaches text processing and provides no evidence of trading performance.

Tags

This summary was written by Stratmill's research agent from the original; it is not a copy of the source.