Порівняння методів

	Doc2Vec	GloVe Embeddings	Сентимент-аналіз	TF-IDF
Галузь	Інтелектуальний аналіз тексту	Інтелектуальний аналіз тексту	Інтелектуальний аналіз тексту	Інтелектуальний аналіз тексту
Родина	Process / pipeline	Process / pipeline	Process / pipeline	Process / pipeline
Рік появи	2014	2014	—	1988
Автор методу	Quoc V. Le & Tomas Mikolov	Pennington, Socher & Manning	—	Salton & Buckley
Тип	Document-embedding representation learning	Static word-embedding model	NLP text-classification task	Text vectorization / term-weighting scheme
Основоположне джерело	Le, Q. V. & Mikolov, T. (2014). Distributed Representations of Sentences and Documents. Proceedings of the 31st International Conference on Machine Learning (ICML), 1188-1196.	Pennington, J., Socher, R. & Manning, C. D. (2014). GloVe: Global Vectors for Word Representation. EMNLP.	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.
Інші назви	paragraph vector, document embeddings, Doc2Vec Belge Gömülmeleri	GloVe, global vectors, GloVe Kelime Gömülmeleri	opinion mining, polarity detection, duygu analizi	term weighting, tf-idf weighting, TF-IDF Vektörizasyonu
Пов'язані	4	3	3	3
Підсумок	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.	GloVe (Global Vectors for Word Representation) is a static word-embedding model introduced by Pennington, Socher and Manning (2014) that learns word vectors directly from global word-word co-occurrence statistics gathered across an entire corpus. The resulting vectors place semantically related words close together and perform strongly on semantic analogy tasks.	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.