  Complaining is a speech act extensively used by humans to communicate a
negative inconsistency between reality and expectations. Previous work on
automatically identifying complaints in social media has focused on using
feature-based and task-specific neural network models. Adapting
state-of-the-art pre-trained neural language models and their combinations with
other linguistic information from topics or sentiment for complaint prediction
has yet to be explored. In this paper, we evaluate a battery of neural models
underpinned by transformer networks which we subsequently combine with
linguistic information. Experiments on a publicly available data set of
complaints demonstrate that our models outperform previous state-of-the-art
methods by a large margin achieving a macro F1 up to 87.
