Automatically Identifying Complaints in Social Media
This work addresses the need for organizations or brands to improve customer experience and develop dialogue systems by identifying complaints, but it is incremental as it applies existing methods to a new dataset.
The paper tackled the problem of automatically identifying complaints in social media by collecting a new annotated dataset from Twitter and training models, achieving up to 79 F1 score across nine domains.
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.