  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.
