Sentiment Analysis Using Aligned Word Embeddings for Uralic Languages
The result's identifiers
Result code in IS VaVaI
<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F00216208%3A11320%2F23%3A3ZLBZFLX" target="_blank" >RIV/00216208:11320/23:3ZLBZFLX - isvavai.cz</a>
Result on the web
<a href="http://arxiv.org/abs/2305.15380" target="_blank" >http://arxiv.org/abs/2305.15380</a>
DOI - Digital Object Identifier
<a href="http://dx.doi.org/10.48550/arXiv.2305.15380" target="_blank" >10.48550/arXiv.2305.15380</a>
Alternative languages
Result language
švédština
Original language name
Sentiment Analysis Using Aligned Word Embeddings for Uralic Languages
Original language description
"In this paper, we present an approach for translating word embeddings from a majority language into 4 minority languages: Erzya, Moksha, Udmurt and Komi-Zyrian. Furthermore, we align these word embeddings and present a novel neural network model that is trained on English data to conduct sentiment analysis and then applied on endangered language data through the aligned word embeddings. To test our model, we annotated a small sentiment analysis corpus for the 4 endangered languages and Finnish. Our method reached at least 56% accuracy for each endangered language. The models and the sentiment corpus will be released together with this paper. Our research shows that state-of-the-art neural models can be used with endangered languages with the only requirement being a dictionary between the endangered language and a majority language."
Czech name
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Czech description
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Classification
Type
O - Miscellaneous
CEP classification
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OECD FORD branch
10201 - Computer sciences, information science, bioinformathics (hardware development to be 2.2, social aspect to be 5.8)
Result continuities
Project
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Continuities
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Others
Publication year
2023
Confidentiality
S - Úplné a pravdivé údaje o projektu nepodléhají ochraně podle zvláštních právních předpisů