TF-IDF (Term Frequency-Inverse Document Frequency) is a text analysis technique used for assessing the importance of a word within a collection of documents, often applied in information retrieval and text mining. The core idea is that the more frequently a word appears in a document and the less it appears across the entire document collection, the more important it is. TF-IDF is commonly used in two main areas:
Text Retrieval: In search engines, TF-IDF is used to determine the relevance of search terms to documents. Words with higher TF-IDF values are ranked higher in search results because they are considered important words that appear frequently in documents but not in all documents, making them relevant to the search query.
Text Mining: TF-IDF is also employed in text mining tasks such as text classification, clustering, and information extraction. By calculating the TF-IDF value for each word, key terms within the text can be identified, aiding in understanding the text’s topic or content.
Term Frequency (TF): It represents how often a word appears in a document, usually calculated as the number of times a word occurs in the document divided by the total number of words in that document. For example, if a word appears 10 times in a document of 1000 words, its TF is 10/1000 = 0.01.
Inverse Document Frequency (IDF): It represents the importance of a word across the entire document collection. IDF is calculated as the total number of documents divided by the number of documents containing the word, followed by taking the logarithm. The use of logarithm helps balance the importance of words and prevents bias towards high-frequency terms. For instance, if a word appears 10 times in a collection of 10000 documents, its IDF is log(10000/10) = 3.
TF-IDF Value: The TF and IDF are multiplied to obtain the TF-IDF value for a word. Using the previous example, if the TF is 0.01 and the IDF is 3, then the TF-IDF value for that word is 0.01 * 3 = 0.03.
By calculating TF-IDF values for all words in a document, you can rank the importance of words within that document and use it for various text analysis tasks. TF-IDF is a simple yet effective method for text feature representation, widely used in natural language processing and information retrieval fields.
“Methods based on Text Analysis” refers to a series of techniques that utilize Natural Language Processing (NLP) and text mining methods to process and analyze textual data. These methods aim to extract valuable information, insights, and knowledge from text to address various tasks and problems.