Compara mètodes

	Doc2Vec	Anàlisi de sentiments	TF-IDF
Camp	Mineria de text	Mineria de text	Mineria de text
Família	Process / pipeline	Process / pipeline	Process / pipeline
Any d'origen	2014	—	1988
Autor original	Quoc V. Le & Tomas Mikolov	—	Salton & Buckley
Tipus	Document-embedding representation learning	NLP text-classification task	Text vectorization / term-weighting scheme
Font seminal	Le, Q. V. & Mikolov, T. (2014). Distributed Representations of Sentences and Documents. Proceedings of the 31st International Conference on Machine Learning (ICML), 1188-1196.	Pang, B. & Lee, L. (2008). Opinion Mining and Sentiment Analysis. Foundations and Trends in Information Retrieval, 2(1-2), 1-135.	Salton, G. & Buckley, C. (1988). Term-weighting approaches in automatic text retrieval. Information Processing & Management, 24(5), 513-523.
Àlies	paragraph vector, document embeddings, Doc2Vec Belge Gömülmeleri	opinion mining, polarity detection, duygu analizi	term weighting, tf-idf weighting, TF-IDF Vektörizasyonu
Relacionats	4	3	3
Resum	Doc2Vec, also known as Paragraph Vector, is a representation-learning method introduced by Le and Mikolov (2014) that maps whole documents to fixed-length dense vectors. These vectors place similar documents close together in space, supporting document comparison and classification.	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.	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.