It is often used as a weighting factor in text searches and classification learning algorithms, and can be applied at either the document level or the sentence level.
In simple terms, TF-IDF measures the frequency of a term (word) within a given document compared to its overall frequency across all documents in a corpus. This helps to identify words that are more significant for a particular topic or theme within the collection. For example, if we have a large dataset containing news articles about politics and economics, TF-IDF can help us to identify which terms (such as “inflation,” “recession,” or “election”) are most important in distinguishing between these two topics.
To calculate TF-IDF for a given word w in document d within corpus C:
1. Calculate the frequency of term w in document d, denoted by tf(w,d). This is simply the number of times that the term appears in the document.
2. Calculate the total number of terms (words) in all documents in the corpus, denoted by N.
3. Calculate the frequency of term w across all documents in the corpus, denoted by df(w). This can be found using a simple count or by querying an index that has already been built for this purpose.
4. Calculate the inverse document frequency (IDF) for term w: idf(w) = log10((N / df(w)) + 1). The IDF is used to downweight terms that appear frequently across all documents in the corpus, as these are less informative and may be more common stopwords.
5. Calculate the TF-IDF score for term w in document d: tfidf(w,d) = tf(w,d) * idf(w). This gives us a weighted value that reflects both the frequency of the term within the document and its importance relative to other terms across all documents.
In practice, TF-IDF can be used in various ways depending on the specific application or task at hand. For example:
1. In text classification tasks (such as sentiment analysis), we might use TF-IDF to weight each term based on its importance and then apply a machine learning algorithm (such as logistic regression) to predict the overall sentiment of the document.
2. In information retrieval applications, we might use TF-IDF to rank search results according to their relevance or significance for a given query. This can help us to identify documents that are most likely to contain the desired information or answer our question.
3. In text summarization tasks (such as news article headline generation), we might use TF-IDF to select the most important terms and phrases from each document, based on their frequency and importance relative to other terms in the corpus. This can help us to generate accurate and informative headlines that accurately reflect the content of the underlying text.
Overall, TF-IDF is a powerful tool for working with natural language data and can be used in many different ways depending on the specific application or task at hand. By combining frequency information (TF) with document-level context (IDF), we can create more accurate and informative models that are better suited to the needs of real-world applications.