Vertaile menetelmiä

	Huomiomekanismi	BERT-upotukset – kontekstisidonnaiset tekstiesitykset	Sentiment Analysis
Tieteenala	Syväoppiminen	Tekstinlouhinta	Tekstinlouhinta
Menetelmäperhe	Machine learning	Process / pipeline	Process / pipeline
Syntyvuosi	2015	2019	—
Kehittäjä	Bahdanau, D.; Luong, M.T.	Devlin, Chang, Lee & Toutanova (Google AI)	—
Tyyppi	Neural attention layer (encoder-decoder)	Contextual transformer text-representation method	NLP text-classification task
Alkuperäislähde	Bahdanau, D., Cho, K. & Bengio, Y. (2015). Neural Machine Translation by Jointly Learning to Align and Translate. ICLR.	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	Dikkat Mekanizması (Bahdanau / Luong Attention), dikkat mekanizmasi, neural attention, additive attention	contextual embeddings, transformer embeddings, BERT Tabanlı Metin Gömülmeleri	opinion mining, polarity detection, duygu analizi
Liittyvät	5	4	3
Tiivistelmä	The attention mechanism, introduced by Bahdanau, Cho and Bengio in 2015 and refined by Luong, Pham and Manning the same year, lets a sequence decoder dynamically learn which of the encoder's outputs to focus on at each step. Before the Transformer, it substantially improved machine-translation quality by freeing models from compressing an entire input into a single fixed vector.	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.