Vertaile menetelmiä

	Dokumenttien klusterointi	Semanttinen samankaltaisuus – Merkityksen mittaaminen tekstien välillä	Sentiment Analysis
Tieteenala	Tekstinlouhinta	Tekstinlouhinta	Tekstinlouhinta
Menetelmäperhe	Process / pipeline	Process / pipeline	Process / pipeline
Syntyvuosi	—	2019	—
Kehittäjä	—	Nils Reimers & Iryna Gurevych (Sentence-BERT)	—
Tyyppi	Unsupervised text-mining task	NLP text-comparison task	NLP text-classification task
Alkuperäislähde	Aggarwal, C. C. & Zhai, C. (2012). Mining Text Data. Springer. ISBN: 9781461432227	Reimers, N. & Gurevych, I. (2019). Sentence-BERT: Sentence Embeddings using Siamese BERT-Networks. EMNLP.	Pang, B. & Lee, L. (2008). Opinion Mining and Sentiment Analysis. Foundations and Trends in Information Retrieval, 2(1-2), 1-135.
Rinnakkaisnimet	text clustering, unsupervised text grouping, Belge Kümeleme (Document Clustering)	semantic textual similarity, text similarity, Anlamsal Benzerlik Analizi	opinion mining, polarity detection, duygu analizi
Liittyvät	4	4	3
Tiivistelmä	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).	Semantic similarity analysis measures how close in meaning two texts are, rather than how many words they share on the surface. Building on the Sentence-BERT work of Reimers and Gurevych (2019), it represents each text as a vector and compares those vectors so that paraphrases score high even when their wording differs.	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.