Preoţiuc-Pietro, D., Găman, M. and Aletras, N. (2019) Automatically identifying complaints in social media. In: Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics. 57th Annual Meeting of the Association for Computational Linguistics, 28 Jul - 02 Aug 2019, Florence, Italy. ACL , pp. 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 in English 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.
Metadata
Item Type: | Proceedings Paper |
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Authors/Creators: |
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Copyright, Publisher and Additional Information: | © 2019 Association for Computational Linguistics. This is an Open Access article distributed under the terms of the Creative Commons Attribution Licence (http://creativecommons.org/licenses/by/4.0), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. |
Keywords: | Computation and Language; Social and Information Networks |
Dates: |
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Institution: | The University of Sheffield |
Academic Units: | The University of Sheffield > Faculty of Engineering (Sheffield) > Department of Computer Science (Sheffield) |
Depositing User: | Symplectic Sheffield |
Date Deposited: | 31 Jul 2019 13:39 |
Last Modified: | 09 Jan 2020 15:05 |
Status: | Published |
Publisher: | ACL |
Refereed: | Yes |
Identification Number: | 10.18653/v1/P19-1495 |
Open Archives Initiative ID (OAI ID): | oai:eprints.whiterose.ac.uk:149065 |