Võrdle meetodeid

	Dokumentide klasterdamine	Sentimentanalüüs	Word2Vec
Valdkond	Tekstikaeve	Tekstikaeve	Tekstikaeve
Perekond	Process / pipeline	Process / pipeline	Process / pipeline
Tekkeaasta	—	—	2013
Looja	—	—	Tomas Mikolov et al.
Tüüp	Unsupervised text-mining task	NLP text-classification task	Neural word-embedding model
Algallikas	Aggarwal, C. C. & Zhai, C. (2012). Mining Text Data. Springer. ISBN: 9781461432227	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.
Rööpnimetused	text clustering, unsupervised text grouping, Belge Kümeleme (Document Clustering)	opinion mining, polarity detection, duygu analizi	word embeddings, skip-gram, continuous bag-of-words, Word2Vec Kelime Gömülmeleri
Seotud	4	3	4
Kokkuvõte	Document clustering is an unsupervised text-mining task that groups documents with similar content together without using any labels. It is used to organise large collections and for exploratory analysis, drawing on the body of text-mining techniques consolidated by Aggarwal and Zhai (2012) and compared empirically by Steinbach, Karypis and Kumar (2000).	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.