Jämför metoder

	Ämnesmodellering	Sentimentanalys	Word2Vec
Ämnesområde	Textutvinning	Textutvinning	Textutvinning
Familj	Process / pipeline	Process / pipeline	Process / pipeline
Ursprungsår	2003	—	2013
Upphovsperson	Blei, Ng & Jordan	—	Tomas Mikolov et al.
Typ	Generative probabilistic topic model	NLP text-classification task	Neural word-embedding model
Ursprungskälla	Blei, D.M., Ng, A.Y. & Jordan, M.I. (2003). Latent Dirichlet Allocation. Journal of Machine Learning Research, 3, 993-1022.	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	LDA, latent Dirichlet allocation, Konu Modelleme — LDA	opinion mining, polarity detection, duygu analizi	word embeddings, skip-gram, continuous bag-of-words, Word2Vec Kelime Gömülmeleri
Närliggande	4	3	4
Sammanfattning	Latent Dirichlet Allocation (LDA) is a generative probabilistic model introduced by Blei, Ng and Jordan (2003) that extracts the hidden topic distributions underlying a collection of documents. It treats each document as a mixture of latent topics and each topic as a distribution over words, turning an unlabelled corpus into interpretable themes.	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.