# Complaint Identification in Social Media with Transformer Networks

arXiv: https://arxiv.org/abs/2010.10910 · HTML: https://mlsanity.com/paper/2010.10910/

## Key facts

- Cited by 6 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 21 Oct 2020.

## Built on by (most cited first, 6 of 6)

- [Automatic Identification and Classification of Bragging in Social Media](https://mlsanity.com/paper/2203.05840/) (2022)
- [Calibration of Transformer-based Models for Identifying Stress and Depression in Social Media](https://mlsanity.com/paper/2305.16797/) (2023)
- [Point-of-Interest Type Prediction using Text and Images](https://mlsanity.com/paper/2109.00602/) (2021)
- [Modeling the Severity of Complaints in Social Media](https://mlsanity.com/paper/2103.12428/) (2021)
- [Multitask learning for recognizing stress and depression in social media](https://mlsanity.com/paper/2305.18907/) (2023)
- [Towards Expectation Detection in Language: A Case Study on Treatment Expectations in Reddit](https://mlsanity.com/paper/2602.15504/) (2026)

## Abstract (arXiv)

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.