英文文本TFIDF提取关键词

TFIDF (Term Frequency-Inverse Document Frequency) is a statistical measure used to evaluate how important a word is to a document in a collection or corpus. It is commonly used for keyword extraction in natural language processing and information retrieval.

The TFIDF formula for a term t in a document d is given by:

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

where TF (t,d) is the frequency of term t in document d, and IDF (t) is the inverse document frequency of term t, which is calculated as:

IDF (t) = log (N / n)

where N is the total number of documents in the corpus, and n is the number of documents in the corpus that contain the term t.

Using TFIDF, we can extract the most important keywords from a document or corpus by calculating the TFIDF score for each term and selecting the top N terms with the highest scores.

For example, suppose we have a corpus of 100 documents, and we want to extract the top 10 keywords from a specific document. We first calculate the TFIDF score for each term in the document, and then select the top 10 terms with the highest scores. These terms are likely to be the most important keywords in the document.

TFIDF can be used for a variety of natural language processing tasks, including text classification, information retrieval, and sentiment analysis. It is a powerful tool for extracting meaningful information from large collections of text data.