  Background: Neural networks produce biased classification results due to
correlation bias (they learn correlations between their inputs and outputs to
classify samples, even when those correlations do not represent
cause-and-effect relationships).
  Objective: This study introduces a fully unsupervised method of mitigating
correlation bias, demonstrated with sentiment classification on COVID-19 social
media data.
  Methods: Correlation bias in sentiment classification often arises in
conversations about controversial topics. Therefore, this study uses
adversarial learning to contrast clusters based on sentiment classification
labels, with clusters produced by unsupervised topic modeling. This discourages
the neural network from learning topic-related features that produce biased
classification results.
  Results: Compared to a baseline classifier, neural contrastive clustering
approximately doubles accuracy on bias-prone sentences for human-labeled
COVID-19 social media data, without adversely affecting the classifier's
overall F1 score. Despite being a fully unsupervised approach, neural
contrastive clustering achieves a larger improvement in accuracy on bias-prone
sentences than a supervised masking approach.
  Conclusions: Neural contrastive clustering reduces correlation bias in
sentiment text classification. Further research is needed to explore
generalizing this technique to other neural network architectures and
application domains.
