Publication: Neural-network method for determining text author's sentiment to an aspect specified by the named entity
dc.contributor.author | Naumov, A. | |
dc.contributor.author | Rybka, R. | |
dc.contributor.author | Sboev, A. | |
dc.contributor.author | Selivanov, A. | |
dc.contributor.author | Gryaznov, A. | |
dc.contributor.author | Рыбка, Роман Борисович | |
dc.contributor.author | Сбоев, Александр Георгиевич | |
dc.date.accessioned | 2024-11-27T08:25:49Z | |
dc.date.available | 2024-11-27T08:25:49Z | |
dc.date.issued | 2020 | |
dc.description.abstract | © 2020 Copyright for this paper by its authors.This study presents the approach to aspect-based sentiment analysis where a named entity of a certain category is considered as an aspect. Such task formulation is a novelty and opens up the opportunity to determine writers' attitudes to organizations and people considered in texts. This task required a dataset of Russian-language sentences where sentiment with respect to certain named entities would be labeled, which we collected using a crowdsourcing platform. Sentiment determination is based on a deep neural network with attention mechanism and ELMo language model for word vector representation. The proposed model is validated on available data on a similar task. The resulting performance (by the f1-micro metric) on the collected dataset is 0.72, which is the new state of the art for the Russian language. | |
dc.format.extent | С. 134-143 | |
dc.identifier.citation | Neural-network method for determining text author's sentiment to an aspect specified by the named entity / Naumov, A. [et al.] // CEUR Workshop Proceedings. - 2020. - 2648. - P. 134-143 | |
dc.identifier.uri | https://www.scopus.com/record/display.uri?eid=2-s2.0-85092329377&origin=resultslist | |
dc.identifier.uri | https://openrepository.mephi.ru/handle/123456789/22431 | |
dc.relation.ispartof | CEUR Workshop Proceedings | |
dc.title | Neural-network method for determining text author's sentiment to an aspect specified by the named entity | |
dc.type | Conference Paper | |
dspace.entity.type | Publication | |
oaire.citation.volume | 2648 | |
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