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	Doc2Vec	Sentimentanalys
Ämnesområde	Textutvinning	Textutvinning
Familj	Process / pipeline	Process / pipeline
Ursprungsår	2014	—
Upphovsperson	Quoc V. Le & Tomas Mikolov	—
Typ	Document-embedding representation learning	NLP text-classification task
Ursprungskälla	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.
Alias	paragraph vector, document embeddings, Doc2Vec Belge Gömülmeleri	opinion mining, polarity detection, duygu analizi
Närliggande	4	3
Sammanfattning	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.