Sentiment Analysis Using Aligned Word Embeddings for Uralic Languages
Khalid Alnajjar Mika Hämäläinen Jack Rueter
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.