Term Frequency- Inverse Document Frequency (TF-IDF) is a statistical measure used in information retrieval and text mining. It is often used as a weighting factor in search algorithms, document classification, and text clustering.
TF-IDF is calculated by multiplying two factors: the term frequency and the inverse document frequency. The term frequency is the number of times a term appears in a document. The inverse document frequency is a measure of how often a term appears in a collection of documents.
TF-IDF can be used to find the most important terms in a document or set of documents. It can also be used to find documents that are similar to each other.
There are many variants of TF-IDF. The most common variant is called Term Frequency times Inverse Document Frequency (TF*IDF).
TF-IDF is usually applied to a corpus of documents. However, it can also be applied to other data such as website clickstreams and social media posts.
There are many software packages that implement TF-IDF. Some examples include:
- Apache Lucene
- Elasticsearch
- Solr
- scikit-learn
- gensim
TF-IDF is a valuable tool for text analytics. It can be used to find the most important terms in a document or set of documents. It can also be used to find documents that are similar to each other. TF-IDF is usually applied to a corpus of documents but it can also be applied to other data such as website clickstreams and social media posts. There are many software packages that implement TF-IDF including Apache Lucene, Elasticsearch, Solr, scikit-learn, and gensim.
Other variants include:
- BM25, a variation of TF-IDF used in information retrieval
- log(TF) * IDF, a variant used in text mining
- Boolean TF-IDF, a variant used in document classification
Benefits of TF-IDF :
- improves retrieval precision
- reduces the need for manual keyword selection
Disadvantages of TF-IDF:
- requires a large amount of data in order to be effective
- computationally expensive to calculate