Automatically Identifying Complaints in Social Media
June 10, 2019 ยท Declared Dead ยท ๐ Annual Meeting of the Association for Computational Linguistics
Authors
Daniel Preotiuc-Pietro, Mihaela Gaman, Nikolaos Aletras
arXiv ID
1906.03890
Category
cs.CL: Computation & Language
Cross-listed
cs.SI
Citations
68
Venue
Annual Meeting of the Association for Computational Linguistics
Repository
https://github.com/danielpreotiuc/complaints-social-media}}
Last Checked
1 month ago
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.\footnote{Data and code is available here: \url{https://github.com/danielpreotiuc/complaints-social-media}} 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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