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PolSentiLex: Sentiment Detection in Socio-Political Discussions on Russian Social Media Read chapter Read first chapter

2020 | OriginalPaper | Chapter

Improving Results on Russian Sentiment Datasets

Authors : Anton Golubev, Natalia Loukachevitch

Published in: Artificial Intelligence and Natural Language

Publisher: Springer International Publishing

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Abstract

In this study, we test standard neural network architectures (CNN, LSTM, BiLSTM) and recently appeared BERT architectures on previous Russian sentiment evaluation datasets. We compare two variants of Russian BERT and show that for all sentiment tasks in this study the conversational variant of Russian BERT performs better. The best results were achieved by BERT-NLI model, which treats sentiment classification tasks as a natural language inference task. On one of the datasets, this model practically achieves the human level .

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previous chapter Predicting Eurovision Song Contest Results Using Sentiment Analysis
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Footnotes
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Metadata
Title
Improving Results on Russian Sentiment Datasets
Authors
Anton Golubev
Natalia Loukachevitch
Copyright Year
2020
Book
Artificial Intelligence and Natural Language

Print ISBN: 978-3-030-59081-9

Electronic ISBN: 978-3-030-59082-6

Copyright Year: 2020

DOI
https://doi.org/10.1007/978-3-030-59082-6_8

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