@inproceedings{gimadi-2021-web-sentiment,
title = "Web-sentiment analysis of public comments (public reviews) for languages with limited resources such as the {K}azakh language",
author = "Gimadi, Dinara",
editor = "Djabri, Souhila and
Gimadi, Dinara and
Mihaylova, Tsvetomila and
Nikolova-Koleva, Ivelina",
booktitle = "Proceedings of the Student Research Workshop Associated with RANLP 2021",
month = sep,
year = "2021",
address = "Online",
publisher = "INCOMA Ltd.",
url = "https://aclanthology.org/2021.ranlp-srw.10",
pages = "65--68",
abstract = "In the pandemic period, the stay-at-home trend forced businesses to switch their activities to digital mode, for example, app-based payment methods, social distancing via social media platforms, and other digital means have become an integral part of our lives. Sentiment analysis of textual information in user comments is a topical task in emotion AI because user comments or reviews are not homogeneous, they contain sparse context behind, and are misleading both for human and computer. Barriers arise from the emotional language enriched with slang, peculiar spelling, transliteration, use of emoji and their symbolic counterparts, and code-switching. For low resource languages sentiment analysis has not been worked upon extensively, because of an absence of ready-made tools and linguistic resources for sentiment analysis. This research focuses on developing a method for aspect-based sentiment analysis for Kazakh-language reviews in Android Google Play Market.",
}
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%0 Conference Proceedings
%T Web-sentiment analysis of public comments (public reviews) for languages with limited resources such as the Kazakh language
%A Gimadi, Dinara
%Y Djabri, Souhila
%Y Gimadi, Dinara
%Y Mihaylova, Tsvetomila
%Y Nikolova-Koleva, Ivelina
%S Proceedings of the Student Research Workshop Associated with RANLP 2021
%D 2021
%8 September
%I INCOMA Ltd.
%C Online
%F gimadi-2021-web-sentiment
%X In the pandemic period, the stay-at-home trend forced businesses to switch their activities to digital mode, for example, app-based payment methods, social distancing via social media platforms, and other digital means have become an integral part of our lives. Sentiment analysis of textual information in user comments is a topical task in emotion AI because user comments or reviews are not homogeneous, they contain sparse context behind, and are misleading both for human and computer. Barriers arise from the emotional language enriched with slang, peculiar spelling, transliteration, use of emoji and their symbolic counterparts, and code-switching. For low resource languages sentiment analysis has not been worked upon extensively, because of an absence of ready-made tools and linguistic resources for sentiment analysis. This research focuses on developing a method for aspect-based sentiment analysis for Kazakh-language reviews in Android Google Play Market.
%U https://aclanthology.org/2021.ranlp-srw.10
%P 65-68
Markdown (Informal)
[Web-sentiment analysis of public comments (public reviews) for languages with limited resources such as the Kazakh language](https://aclanthology.org/2021.ranlp-srw.10) (Gimadi, RANLP 2021)
ACL