Methoden vergleichen

	Semantische Ähnlichkeit	Dokumenten-Clustering	Sentiment-Analyse
Fachgebiet	Text Mining	Text Mining	Text Mining
Familie	Process / pipeline	Process / pipeline	Process / pipeline
Entstehungsjahr	2019	—	—
Urheber	Nils Reimers & Iryna Gurevych (Sentence-BERT)	—	—
Typ	NLP text-comparison task	Unsupervised text-mining task	NLP text-classification task
Wegweisende Quelle	Reimers, N. & Gurevych, I. (2019). Sentence-BERT: Sentence Embeddings using Siamese BERT-Networks. EMNLP.	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.
Aliasnamen	semantic textual similarity, text similarity, Anlamsal Benzerlik Analizi	text clustering, unsupervised text grouping, Belge Kümeleme (Document Clustering)	opinion mining, polarity detection, duygu analizi
Verwandt	4	4	3
Zusammenfassung	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.	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.