# Incorporating Emotions into Health Mention Classification Task on Social Media

arXiv: https://arxiv.org/abs/2212.05039 · HTML: https://mlsanity.com/paper/2212.05039/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 14 of its 42 references that are in the map.
- First posted on arXiv 9 Dec 2022.

## Builds on (most cited first, 14 of 14)

- [BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding](https://mlsanity.com/paper/1810.04805/) (2018)
- [Adam: A Method for Stochastic Optimization](https://mlsanity.com/paper/1412.6980/) (2014)
- [A Unified Approach to Interpreting Model Predictions](https://mlsanity.com/paper/1705.07874/) (2017)
- ["Why Should I Trust You?": Explaining the Predictions of Any Classifier](https://mlsanity.com/paper/1602.04938/) (2016)
- [Dropout as a Bayesian Approximation: Representing Model Uncertainty in Deep Learning](https://mlsanity.com/paper/1506.02142/) (2015)
- [GoEmotions: A Dataset of Fine-Grained Emotions](https://mlsanity.com/paper/2005.00547/) (2020)
- [Sentence Encoders on STILTs: Supplementary Training on Intermediate Labeled-data Tasks](https://mlsanity.com/paper/1811.01088/) (2018)
- [Did You Really Just Have a Heart Attack? Towards Robust Detection of Personal Health Mentions in Social Media](https://mlsanity.com/paper/1802.09130/) (2018)
- [Rethinking Why Intermediate-Task Fine-Tuning Works](https://mlsanity.com/paper/2108.11696/) (2021)
- [Detecting Perceived Emotions in Hurricane Disasters](https://mlsanity.com/paper/2004.14299/) (2020)
- [Multi-task Learning of Pairwise Sequence Classification Tasks Over Disparate Label Spaces](https://mlsanity.com/paper/1802.09913/) (2018)
- [Figurative Usage Detection of Symptom Words to Improve Personal Health Mention Detection](https://mlsanity.com/paper/1906.05466/) (2019)
- [INTERACTION: A Generative XAI Framework for Natural Language Inference Explanations](https://mlsanity.com/paper/2209.01061/) (2022)
- [Exploring Bayesian Deep Learning for Urgent Instructor Intervention Need in MOOC Forums](https://mlsanity.com/paper/2104.12643/) (2021)

## Abstract (arXiv)

The health mention classification (HMC) task is the process of identifying and classifying mentions of health-related concepts in text. This can be useful for identifying and tracking the spread of diseases through social media posts. However, this is a non-trivial task. Here we build on recent studies suggesting that using emotional information may improve upon this task. Our study results in a framework for health mention classification that incorporates affective features. We present two methods, an intermediate task fine-tuning approach (implicit) and a multi-feature fusion approach (explicit) to incorporate emotions into our target task of HMC. We evaluated our approach on 5 HMC-related datasets from different social media platforms including three from Twitter, one from Reddit and another from a combination of social media sources. Extensive experiments demonstrate that our approach results in statistically significant performance gains on HMC tasks. By using the multi-feature fusion approach, we achieve at least a 3% improvement in F1 score over BERT baselines across all datasets. We also show that considering only negative emotions does not significantly affect performance on the HMC task. Additionally, our results indicate that HMC models infused with emotional knowledge are an effective alternative, especially when other HMC datasets are unavailable for domain-specific fine-tuning. The source code for our models is freely available at https://github.com/tahirlanre/Emotion_PHM.