Comparar métodos

	TF-IDF	Classificação de Texto	Word2Vec
Área	Mineração de texto	Mineração de texto	Mineração de texto
Família	Process / pipeline	Process / pipeline	Process / pipeline
Ano de origem	1988	—	2013
Autor original	Salton & Buckley	—	Tomas Mikolov et al.
Tipo	Text vectorization / term-weighting scheme	Supervised NLP classification task	Neural word-embedding model
Fonte seminal	Salton, G. & Buckley, C. (1988). Term-weighting approaches in automatic text retrieval. Information Processing & Management, 24(5), 513-523.	Joachims, T. (1998). Text Categorization with Support Vector Machines: Learning with Many Relevant Features. ECML 1998. Lecture Notes in Computer Science, vol 1398. Springer.	Mikolov, T., Chen, K., Corrado, G. & Dean, J. (2013). Efficient Estimation of Word Representations in Vector Space.
Outros nomes	term weighting, tf-idf weighting, TF-IDF Vektörizasyonu	text categorization, document classification, topic classification, metin sınıflandırma	word embeddings, skip-gram, continuous bag-of-words, Word2Vec Kelime Gömülmeleri
Relacionados	3	4	4
Resumo	TF-IDF, introduced by Salton and Buckley (1988), is a term-weighting scheme that scores each word in a document by how often it appears there and how rare it is across the whole collection. It turns raw text into weighted document vectors, giving high weight to terms that are frequent in one document but uncommon elsewhere.	Text classification, also called text categorization, is a supervised natural-language-processing task that automatically assigns documents to predefined categories. Building on the support-vector-machine approach to text categorization established by Joachims (1998) and consolidated in the text-mining literature by Aggarwal and Zhai (2012), it powers tasks such as spam detection and topic classification by learning from labelled examples.	Word2Vec is a neural word-embedding technique introduced by Mikolov and colleagues in 2013 that maps each word in a text corpus to a dense numeric vector. Words that appear in similar contexts end up close together in the vector space, so the embeddings capture semantic similarity that can be measured arithmetically.