Vertaile menetelmiä

	Semanttinen samankaltaisuus – Merkityksen mittaaminen tekstien välillä	BERT-upotukset – kontekstisidonnaiset tekstiesitykset	Sentiment Analysis
Tieteenala	Tekstinlouhinta	Tekstinlouhinta	Tekstinlouhinta
Menetelmäperhe	Process / pipeline	Process / pipeline	Process / pipeline
Syntyvuosi	2019	2019	—
Kehittäjä	Nils Reimers & Iryna Gurevych (Sentence-BERT)	Devlin, Chang, Lee & Toutanova (Google AI)	—
Tyyppi	NLP text-comparison task	Contextual transformer text-representation method	NLP text-classification task
Alkuperäislähde	Reimers, N. & Gurevych, I. (2019). Sentence-BERT: Sentence Embeddings using Siamese BERT-Networks. EMNLP.	Devlin, J., Chang, M.-W., Lee, K. & Toutanova, K. (2019). BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding. NAACL-HLT, 4171-4186.	Pang, B. & Lee, L. (2008). Opinion Mining and Sentiment Analysis. Foundations and Trends in Information Retrieval, 2(1-2), 1-135.
Rinnakkaisnimet	semantic textual similarity, text similarity, Anlamsal Benzerlik Analizi	contextual embeddings, transformer embeddings, BERT Tabanlı Metin Gömülmeleri	opinion mining, polarity detection, duygu analizi
Liittyvät	4	4	3
Tiivistelmä	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.	BERT-based text embeddings, introduced by Devlin and colleagues at Google AI in 2019, turn text into context-sensitive dense vectors using a bidirectional Transformer encoder. Because the meaning of a word shifts with its context, BERT produces richer representations than static methods such as Word2Vec or topic models like LDA.	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.