Linganisha mbinu

	Uchanganuzi wa Hisia wa Lugha Nyingi	Sentence Embeddings (Vibandiko vya Sentensi)
Nyanja	Ujifunzaji wa Kina	Ujifunzaji wa Kina
Familia	Machine learning	Machine learning
Mwaka wa asili	2004–2020	2015–2019
Mwanzilishi	Pang, B. & Lee, L. (early sentiment analysis); cross-lingual extension via mBERT/XLM-R community (2019–2020)	Kiros et al. (Skip-Thought, 2015); Reimers & Gurevych (Sentence-BERT, 2019)
Aina	Supervised classification / fine-tuned LM	Representation learning / embedding
Chanzo asilia	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. (2019). Sentence-BERT: Sentence Embeddings using Siamese BERT-Networks. Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing (EMNLP), 3980–3990.
Majina mbadala	cross-lingual sentiment analysis, multilingual opinion mining, multilingual sentiment classification, MSA	sentence vectors, sentence representations, SBERT, semantic sentence encoding
Zinazohusiana	5	4
Muhtasari	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.	Sentence Embeddings convert a sentence or short text into a single fixed-length dense vector that captures its semantic meaning. These vectors allow downstream tasks — semantic similarity, clustering, retrieval, and classification — to operate on numerical representations instead of raw text, making them one of the most versatile building blocks in modern NLP pipelines.