Σύγκριση μεθόδων

	Μηχανισμός Προσοχής	Ανάλυση Συναισθήματος
Πεδίο	Βαθιά Μάθηση	Εξόρυξη Κειμένου
Οικογένεια	Machine learning	Process / pipeline
Έτος προέλευσης	2015	—
Δημιουργός	Bahdanau, D.; Luong, M.T.	—
Τύπος	Neural attention layer (encoder-decoder)	NLP text-classification task
Θεμελιώδης πηγή	Bahdanau, D., Cho, K. & Bengio, Y. (2015). Neural Machine Translation by Jointly Learning to Align and Translate. ICLR.	Pang, B. & Lee, L. (2008). Opinion Mining and Sentiment Analysis. Foundations and Trends in Information Retrieval, 2(1-2), 1-135.
Εναλλακτικές ονομασίες	Dikkat Mekanizması (Bahdanau / Luong Attention), dikkat mekanizmasi, neural attention, additive attention	opinion mining, polarity detection, duygu analizi
Συναφείς	5	3
Σύνοψη	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.	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.