Sammenlign metoder

	Forklarlig Sentimentanalyse	RoBERTa-baseret Klassifikation
Fagområde	Dyb læring	Dyb læring
Familie	Machine learning	Machine learning
Oprindelsesår	2016–2020	2019
Ophavsperson	Multiple contributors (LIME: Ribeiro et al. 2016; SHAP: Lundberg & Lee 2017; attention-based XAI in NLP: numerous, 2018–2020)	Liu, Y. et al. (Facebook AI Research / University of Washington)
Type	Interpretable NLP pipeline	Pre-trained transformer fine-tuned for sequence classification
Oprindelig kilde	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.	Liu, Y., Ott, M., Goyal, N., Du, J., Joshi, M., Chen, D., Levy, O., Lewis, M., Zettlemoyer, L., & Stoyanov, V. (2019). RoBERTa: A Robustly Optimized BERT Pretraining Approach. arXiv preprint arXiv:1907.11692.
Aliasser	XAI sentiment analysis, interpretable sentiment classification, transparent opinion mining, explainable opinion analysis	RoBERTa classifier, RoBERTa text classification, Robustly Optimized BERT Classification, RoBERTa fine-tuning for classification
Relaterede	5	5
Resumé	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.	RoBERTa-based Classification applies the RoBERTa pre-trained transformer — trained more robustly than BERT with dynamic masking and larger batches — to text categorisation tasks by adding a lightweight classification head on top of the [CLS] token representation and fine-tuning the entire model on labelled examples. It consistently matches or outperforms BERT on standard NLP benchmarks.