@inproceedings{preotiuc-pietro-etal-2019-automatically,
title = "Automatically Identifying Complaints in Social Media",
author = "Preo{\c{t}}iuc-Pietro, Daniel and
Gaman, Mihaela and
Aletras, Nikolaos",
editor = "Korhonen, Anna and
Traum, David and
M{\`a}rquez, Llu{\'\i}s",
booktitle = "Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics",
month = jul,
year = "2019",
address = "Florence, Italy",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/P19-1495",
doi = "10.18653/v1/P19-1495",
pages = "5008--5019",
abstract = "Complaining is a basic speech act regularly used in human and computer mediated communication to express a negative mismatch between reality and expectations in a particular situation. Automatically identifying complaints in social media is of utmost importance for organizations or brands to improve the customer experience or in developing dialogue systems for handling and responding to complaints. In this paper, we introduce the first systematic analysis of complaints in computational linguistics. We collect a new annotated data set of written complaints expressed on Twitter. We present an extensive linguistic analysis of complaining as a speech act in social media and train strong feature-based and neural models of complaints across nine domains achieving a predictive performance of up to 79 F1 using distant supervision.",
}
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<abstract>Complaining is a basic speech act regularly used in human and computer mediated communication to express a negative mismatch between reality and expectations in a particular situation. Automatically identifying complaints in social media is of utmost importance for organizations or brands to improve the customer experience or in developing dialogue systems for handling and responding to complaints. In this paper, we introduce the first systematic analysis of complaints in computational linguistics. We collect a new annotated data set of written complaints expressed on Twitter. We present an extensive linguistic analysis of complaining as a speech act in social media and train strong feature-based and neural models of complaints across nine domains achieving a predictive performance of up to 79 F1 using distant supervision.</abstract>
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%0 Conference Proceedings
%T Automatically Identifying Complaints in Social Media
%A Preoţiuc-Pietro, Daniel
%A Gaman, Mihaela
%A Aletras, Nikolaos
%Y Korhonen, Anna
%Y Traum, David
%Y Màrquez, Lluís
%S Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics
%D 2019
%8 July
%I Association for Computational Linguistics
%C Florence, Italy
%F preotiuc-pietro-etal-2019-automatically
%X Complaining is a basic speech act regularly used in human and computer mediated communication to express a negative mismatch between reality and expectations in a particular situation. Automatically identifying complaints in social media is of utmost importance for organizations or brands to improve the customer experience or in developing dialogue systems for handling and responding to complaints. In this paper, we introduce the first systematic analysis of complaints in computational linguistics. We collect a new annotated data set of written complaints expressed on Twitter. We present an extensive linguistic analysis of complaining as a speech act in social media and train strong feature-based and neural models of complaints across nine domains achieving a predictive performance of up to 79 F1 using distant supervision.
%R 10.18653/v1/P19-1495
%U https://aclanthology.org/P19-1495
%U https://doi.org/10.18653/v1/P19-1495
%P 5008-5019
Markdown (Informal)
[Automatically Identifying Complaints in Social Media](https://aclanthology.org/P19-1495) (Preoţiuc-Pietro et al., ACL 2019)
ACL
- Daniel Preoţiuc-Pietro, Mihaela Gaman, and Nikolaos Aletras. 2019. Automatically Identifying Complaints in Social Media. In Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics, pages 5008–5019, Florence, Italy. Association for Computational Linguistics.