방법 비교

	다국어 감성 분석	BERT 기반 분류
분야	딥러닝	딥러닝
계열	Machine learning	Machine learning
기원 연도	2004–2020	2019
창시자	Pang, B. & Lee, L. (early sentiment analysis); cross-lingual extension via mBERT/XLM-R community (2019–2020)	Devlin, J., Chang, M.-W., Lee, K., & Toutanova, K. (Google AI Language)
유형	Supervised classification / fine-tuned LM	Pre-trained language model with fine-tuning
원전	Conneau, A., Khandelwal, K., Goyal, N., Chaudhary, V., Wenzek, G., Guzman, F., Grave, E., Ott, M., Zettlemoyer, L., & Stoyanov, V. (2020). Unsupervised Cross-lingual Representation Learning at Scale. Proceedings of ACL 2020, 8440–8451.	Devlin, J., Chang, M.-W., Lee, K., & Toutanova, K. (2019). BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding. In Proceedings of NAACL-HLT 2019 (pp. 4171–4186). Association for Computational Linguistics.
별칭	cross-lingual sentiment analysis, multilingual opinion mining, multilingual sentiment classification, MSA	BERT classifier, BERT fine-tuning for classification, BERT text classification, BERT-CLS
관련	5	4
요약	Multilingual Sentiment Analysis (MSA) applies deep learning — most commonly a fine-tuned multilingual language model such as mBERT or XLM-RoBERTa — to classify the sentiment polarity (positive, negative, neutral) of text written in two or more languages, enabling opinion mining across language boundaries without building separate models per language.	BERT-based Classification fine-tunes Google's Bidirectional Encoder Representations from Transformers model on a labelled text dataset, replacing the generic pre-trained head with a task-specific classification layer. It exploits deep bidirectional context from hundreds of millions of pre-trained parameters to deliver state-of-the-art accuracy on short- and medium-length text classification tasks with relatively modest amounts of labelled data.