@inproceedings{leonova-zuters-2021-frustration,
title = "Frustration Level Annotation in {L}atvian Tweets with Non-Lexical Means of Expression",
author = "Leonova, Viktorija and
Zuters, Janis",
editor = "Mitkov, Ruslan and
Angelova, Galia",
booktitle = "Proceedings of the International Conference on Recent Advances in Natural Language Processing (RANLP 2021)",
month = sep,
year = "2021",
address = "Held Online",
publisher = "INCOMA Ltd.",
url = "https://aclanthology.org/2021.ranlp-1.93",
pages = "814--823",
abstract = "We present a neural-network-driven model for annotating frustration intensity in customer support tweets, based on representing tweet texts using a bag-of-words encoding after processing with subword segmentation together with non-lexical features. The model was evaluated on tweets in English and Latvian languages, focusing on aspects beyond the pure bag-of-words representations used in previous research. The experimental results show that the model can be successfully applied for texts in a non-English language, and that adding non-lexical features to tweet representations significantly improves performance, while subword segmentation has a moderate but positive effect on model accuracy. Our code and training data are publicly available.",
}
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%0 Conference Proceedings
%T Frustration Level Annotation in Latvian Tweets with Non-Lexical Means of Expression
%A Leonova, Viktorija
%A Zuters, Janis
%Y Mitkov, Ruslan
%Y Angelova, Galia
%S Proceedings of the International Conference on Recent Advances in Natural Language Processing (RANLP 2021)
%D 2021
%8 September
%I INCOMA Ltd.
%C Held Online
%F leonova-zuters-2021-frustration
%X We present a neural-network-driven model for annotating frustration intensity in customer support tweets, based on representing tweet texts using a bag-of-words encoding after processing with subword segmentation together with non-lexical features. The model was evaluated on tweets in English and Latvian languages, focusing on aspects beyond the pure bag-of-words representations used in previous research. The experimental results show that the model can be successfully applied for texts in a non-English language, and that adding non-lexical features to tweet representations significantly improves performance, while subword segmentation has a moderate but positive effect on model accuracy. Our code and training data are publicly available.
%U https://aclanthology.org/2021.ranlp-1.93
%P 814-823
Markdown (Informal)
[Frustration Level Annotation in Latvian Tweets with Non-Lexical Means of Expression](https://aclanthology.org/2021.ranlp-1.93) (Leonova & Zuters, RANLP 2021)
ACL