# Automatically Identifying Complaints in Social Media

## TL;DR

This paper introduces a new dataset and models for automatically detecting complaints in social media, specifically Twitter, to help organizations improve customer service and dialogue systems.

## Contribution

It provides the first systematic linguistic analysis of complaints in social media and develops models achieving up to 79 F1 for complaint detection.

## Key findings

- Achieved up to 79 F1 in complaint detection
- Provided extensive linguistic analysis of complaints
- Collected and annotated a new Twitter complaints dataset

## 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.