Confronta i metodi

	TF-IDF	Analisi del Sentimento	Word2Vec
Campo	Text mining	Text mining	Text mining
Famiglia	Process / pipeline	Process / pipeline	Process / pipeline
Anno di origine	1988	—	2013
Ideatore	Salton & Buckley	—	Tomas Mikolov et al.
Tipo	Text vectorization / term-weighting scheme	NLP text-classification task	Neural word-embedding model
Fonte seminale	Salton, G. & Buckley, C. (1988). Term-weighting approaches in automatic text retrieval. Information Processing & Management, 24(5), 513-523.	Pang, B. & Lee, L. (2008). Opinion Mining and Sentiment Analysis. Foundations and Trends in Information Retrieval, 2(1-2), 1-135.	Mikolov, T., Chen, K., Corrado, G. & Dean, J. (2013). Efficient Estimation of Word Representations in Vector Space.
Alias	term weighting, tf-idf weighting, TF-IDF Vektörizasyonu	opinion mining, polarity detection, duygu analizi	word embeddings, skip-gram, continuous bag-of-words, Word2Vec Kelime Gömülmeleri
Correlati	3	3	4
Sintesi	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.	Sentiment analysis, also called opinion mining, is a natural-language-processing task that detects the emotional tone of text — typically classifying it as positive, negative, or neutral. It turns unstructured opinion text into structured, quantifiable polarity signals using one of three families of approaches: sentiment lexicons, trained machine-learning classifiers, or pretrained transformer models.	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.