Term Frequency-Inverse Document Frequency (TF-IDF) is an algorithm used in text analysis to quantify a document’s importance given a corpus of documents. It is a type of statistical parameter, used to most notably determine the importance of a certain word or phrase in a given document, compared to the whole corpus of documents.
TF-IDF works by first calculating the number of occurrences of a particular word or phrase within a document, called the “term frequency.” The algorithm then calculates how many documents contain that term, called “document frequency.” The TF-IDF score is then calculated by taking the term frequency and dividing it by the document frequency. This helps to identify those words in a document that are more likely to provide context and meaning.
TF-IDF is commonly used in information retrieval and text mining applications. It can be used to identify topics in a document, find relevant documents in a collection, and extract keyword phrases for document summarization.
The TF-IDF metric is useful for a wide range of tasks, such as information retrieval, document clustering, classification, document search, and text summarization. It is also useful for assess the relevance of search engine results. Furthermore, it can be used in document categorization, and is often used to assess the importance of words within documents.
The TF-IDF algorithm is simple yet effective, and has formed the basis of many computing applications. It is especially helpful for large collections of documents, as it can quickly and reliably determine which words are important and which ones are not. As such, it is an invaluable tool in computer-aided language processing.
