방법 비교

	BERT 임베딩	Doc2Vec	감성 분석	Word2Vec
분야	텍스트 마이닝	텍스트 마이닝	텍스트 마이닝	텍스트 마이닝
계열	Process / pipeline	Process / pipeline	Process / pipeline	Process / pipeline
기원 연도	2019	2014	—	2013
창시자	Devlin, Chang, Lee & Toutanova (Google AI)	Quoc V. Le & Tomas Mikolov	—	Tomas Mikolov et al.
유형	Contextual transformer text-representation method	Document-embedding representation learning	NLP text-classification task	Neural word-embedding model
원전	Devlin, J., Chang, M.-W., Lee, K. & Toutanova, K. (2019). BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding. NAACL-HLT, 4171-4186.	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.	Mikolov, T., Chen, K., Corrado, G. & Dean, J. (2013). Efficient Estimation of Word Representations in Vector Space.
별칭	contextual embeddings, transformer embeddings, BERT Tabanlı Metin Gömülmeleri	paragraph vector, document embeddings, Doc2Vec Belge Gömülmeleri	opinion mining, polarity detection, duygu analizi	word embeddings, skip-gram, continuous bag-of-words, Word2Vec Kelime Gömülmeleri
관련	4	4	3	4
요약	BERT-based text embeddings, introduced by Devlin and colleagues at Google AI in 2019, turn text into context-sensitive dense vectors using a bidirectional Transformer encoder. Because the meaning of a word shifts with its context, BERT produces richer representations than static methods such as Word2Vec or topic models like LDA.	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.	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.