TF-IDF stands for Term Frequency-Inverse Document Frequency, a statistical measure widely used in information retrieval and text mining to evaluate how important a word is within a document relative to a collection of documents, or a corpus. Essentially, TF-IDF helps to identify the most relevant keywords in a document by balancing two factors: term frequency and inverse document frequency.
Term Frequency (TF)
Term Frequency (TF) calculates how often a particular word (term) appears in a single document. The more times a word occurs in a document, the higher its term frequency. However, it is often normalized to prevent bias toward longer documents. For example, a term that appears 5 times in a 100-word document has a higher TF value than if it appears 5 times in a 1000-word document.
Mathematically, TF is expressed as:
TF = (Number of occurrences of the term in the document) / (Total number of terms in the document)
Inverse Document Frequency (IDF)
Inverse Document Frequency (IDF) assesses how unique or rare a term is across all documents in the corpus. A term that appears in many documents has a lower IDF value because it’s less unique. Conversely, terms that occur in fewer documents will have a higher IDF score, making them more significant. This prevents commonly used words, like “the” or “is,” from being considered important.
The formula for IDF is:
IDF = log(Total number of documents / Number of documents containing the term)
TF-IDF
TF-IDF combines both term frequency and inverse document frequency to highlight important terms that appear frequently in a document but are rare across the corpus. The formula is simply:
TF-IDF = TF * IDF
This measure helps in tasks like document ranking, search engine algorithms, and text summarization. In these applications, words with a high TF-IDF score are more likely to be considered relevant or informative.
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Frequently Asked Questions
Q1. What is TF-IDF used for?
A1: TF-IDF is used to find relevant keywords in text, particularly for ranking documents in search engines, text analysis, and summarization.
Q2. How is TF-IDF different from just counting word frequency?
A2: Unlike basic word frequency, TF-IDF also considers how rare or common a term is across multiple documents, giving more importance to unique terms.
Q3. Can TF-IDF handle stop words like “the” or “is”?
A3: Yes, TF-IDF naturally assigns lower weights to common words like “the” because they appear frequently across many documents, making them less important.
Q4. Where is TF-IDF applied in real life?
A4: TF-IDF is used in search engines, recommender systems, content categorization, and spam detection systems.
Q5. What are the limitations of TF-IDF?
A5: TF-IDF doesn’t capture the context or meaning of words, and it may struggle with polysemous terms (words with multiple meanings) or synonyms.