TFIDFviktning

TFIDF, short for Term Frequency-Inverse Document Frequency, is a numerical statistic used to reflect the importance of a word in a document relative to a collection of documents. It is commonly used in information retrieval and text mining to evaluate the relevance of a term to a document within a corpus.

The TFIDF weight is composed of two main components: Term Frequency (TF) and Inverse Document Frequency (IDF). Term Frequency measures how frequently a term appears in a document, while Inverse Document Frequency measures how important a term is by considering its occurrence across multiple documents. The product of these two components gives the TFIDF score.

TFIDF is particularly useful in natural language processing (NLP) tasks such as document classification, information retrieval, and text summarization. It helps in identifying the most relevant terms in a document by down-weighting common terms that appear frequently across all documents and up-weighting terms that are unique to a specific document.

The formula for calculating TFIDF is as follows:

TFIDF(t, d, D) = TF(t, d) * IDF(t, D)

where:

- TF(t, d) is the term frequency of term t in document d.

- IDF(t, D) is the inverse document frequency of term t in the corpus D.

The term frequency (TF) is often calculated as the raw count of a term in a document, or normalized by the document length to account for varying document sizes. The inverse document frequency (IDF) is typically calculated as the logarithm of the total number of documents divided by the number of documents containing the term.

TFIDF weighting is effective in highlighting the significance of terms within a document context, making it a valuable tool in various text analysis applications.