Methoden vergleichen

	Semantische Ähnlichkeit	Sentiment-Analyse	TF-IDF
Fachgebiet	Text Mining	Text Mining	Text Mining
Familie	Process / pipeline	Process / pipeline	Process / pipeline
Entstehungsjahr	2019	—	1988
Urheber	Nils Reimers & Iryna Gurevych (Sentence-BERT)	—	Salton & Buckley
Typ	NLP text-comparison task	NLP text-classification task	Text vectorization / term-weighting scheme
Wegweisende Quelle	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.	Salton, G. & Buckley, C. (1988). Term-weighting approaches in automatic text retrieval. Information Processing & Management, 24(5), 513-523.
Aliasnamen	semantic textual similarity, text similarity, Anlamsal Benzerlik Analizi	opinion mining, polarity detection, duygu analizi	term weighting, tf-idf weighting, TF-IDF Vektörizasyonu
Verwandt	4	3	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.	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.	TF-IDF, introduced by Salton and Buckley (1988), is a term-weighting scheme that scores each word in a document by how often it appears there and how rare it is across the whole collection. It turns raw text into weighted document vectors, giving high weight to terms that are frequent in one document but uncommon elsewhere.