手法を比較

	多言語感情分析	多言語文埋め込み
分野	深層学習	深層学習
系統	Machine learning	Machine learning
提唱年	2004–2020	2019–2022
提唱者	Pang, B. & Lee, L. (early sentiment analysis); cross-lingual extension via mBERT/XLM-R community (2019–2020)	Reimers, N. & Gurevych, I.; Feng, F. et al. (Google)
種類	Supervised classification / fine-tuned LM	Cross-lingual representation learning
原典	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.	Reimers, N. & Gurevych, I. (2020). Making Monolingual Sentence Embeddings Multilingual using Knowledge Distillation. Proceedings of EMNLP 2020, 4512–4525.
別名	cross-lingual sentiment analysis, multilingual opinion mining, multilingual sentiment classification, MSA	multilingual sentence representations, cross-lingual sentence embeddings, mSE, multilingual semantic embeddings
関連	5	5
概要	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.	Multilingual sentence embeddings map sentences from many languages into a single shared vector space so that semantically equivalent sentences — regardless of language — land close together. Models such as LaBSE, multilingual Sentence-BERT, and mUSE have made it practical to compare, retrieve, and classify text across 50 to 100+ languages without translating anything first.