Abstract
The paper describes the approaches to verification of sentiment frames in the RuSentiFrames lexicon describing sentiment connotations related to specific Russian predicates. Two approaches for verification were used: 1) analysis of specific sentences from Russian National Corpus, 2) via crowdsourcing platform Yandex.Toloka. The idea was to find similarities and differences between the annotations made by the experts in RuSentiFrames and by non-experts from Yandex.Toloka, thus verifying the RuSentiFrames descriptions. The first approach showed that implicit information influences greatly on the author’s attitude and that the context plays crucial role. The second approach showed mostly the agreement between the expert’s and non-expert’s annotations in case of relations between the participants in sentiment frames, but the author’s attitudes were estimated differently in some cases.
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The reported study was funded by RFBR according to the research project № 20-07-01059.
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Matueva, I., Loukachevitch, N. (2021). Methods for Verification of Sentiment Frames. In: van der Aalst, W.M.P., et al. Recent Trends in Analysis of Images, Social Networks and Texts. AIST 2020. Communications in Computer and Information Science, vol 1357. Springer, Cham. https://doi.org/10.1007/978-3-030-71214-3_5
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