Σύγκριση μεθόδων

	Επεξηγήσιμη Ανάλυση Συναισθήματος	Μοντελοποίηση Θεμάτων
Πεδίο	Βαθιά Μάθηση	Βαθιά Μάθηση
Οικογένεια	Machine learning	Machine learning
Έτος προέλευσης	2016–2020	1999–2003
Δημιουργός	Multiple contributors (LIME: Ribeiro et al. 2016; SHAP: Lundberg & Lee 2017; attention-based XAI in NLP: numerous, 2018–2020)	Hofmann, T. (pLSA, 1999); Blei, D. M., Ng, A. Y., & Jordan, M. I. (LDA, 2003)
Τύπος	Interpretable NLP pipeline	Unsupervised generative probabilistic model
Θεμελιώδης πηγή	Danilevsky, M., Qian, K., Aharonov, R., Katsis, Y., Kawas, B., & Sen, P. (2020). A Survey of the State of Explainable AI for Natural Language Processing. Proceedings of the 1st Conference of the Asia-Pacific Chapter of the ACL and the 10th IJCNLP, 447–459.	Blei, D. M., Ng, A. Y., & Jordan, M. I. (2003). Latent Dirichlet Allocation. Journal of Machine Learning Research, 3, 993–1022.
Εναλλακτικές ονομασίες	XAI sentiment analysis, interpretable sentiment classification, transparent opinion mining, explainable opinion analysis	Latent Semantic Analysis, probabilistic topic modeling, topic discovery, thematic modeling
Συναφείς	5	5
Σύνοψη	Explainable sentiment analysis pairs a sentiment classification model — typically a fine-tuned transformer such as BERT or RoBERTa — with a post-hoc or intrinsic explanation method (SHAP, LIME, attention visualization, or integrated gradients) that reveals which words, phrases, or features drove each prediction. The goal is both high predictive accuracy and transparent, auditable rationales for every label.	Topic Modeling is a family of unsupervised probabilistic techniques for discovering latent thematic structure in large text collections. By learning which words tend to co-occur, models such as Latent Dirichlet Allocation (LDA) automatically surface coherent topics — each represented as a distribution over vocabulary — without requiring labelled data.