The term "TF-IDF Analysis" (Term Frequency-Inverse Document Frequency) refers to a method from the field of text analysis and information retrieval. It is used to determine the relevance of a term within a document in relation to a larger document collection (corpus). The more frequently a term appears in a single document (Term Frequency, TF) and the rarer it appears in other documents in the corpus (Inverse Document Frequency, IDF), the higher its TF-IDF value—and therefore its relevance for that document.
TF-IDF is commonly used in search engines, text mining applications, or automated text classification to identify the most important terms in a document.
Text Preprocessing: Tokenization, stop word filtering, normalization (e.g., lowercasing, lemmatization).
TF Calculation: Determining how frequently a term occurs within a document.
IDF Calculation: Assessing how rare or frequent a term is across the entire document corpus.
Weighting and Ranking: Computing the TF-IDF value to prioritize terms.
Relevance Analysis: Identifying key terms that characterize a document.
Comparison and Classification: Comparing documents based on their TF-IDF vectors for thematic categorization or similarity analysis.
Visualization: Displaying a document’s most relevant terms as word clouds or relevance charts.
Export and Integration: Providing analysis results for further use in search engines, AI models, or business intelligence tools.
Identifying the most relevant terms in a contract or report.
Weighting keywords to improve SEO strategies.
Analyzing customer feedback to detect frequently mentioned topics or issues.
Preprocessing text data for machine learning applications (e.g., text classification).
Creating thematic clusters by comparing documents with similar term patterns.
Extracting keywords for automated tagging in content management systems.
