Neural Contrastive Clustering: Fully Unsupervised Bias Reduction for Sentiment Classification

TL;DR

This paper introduces a fully unsupervised neural contrastive clustering method that reduces correlation bias in sentiment classification, especially on controversial topics like COVID-19 social media data, without needing labeled data.

Contribution

It presents a novel unsupervised adversarial learning approach that effectively mitigates correlation bias in neural network sentiment classifiers, outperforming some supervised methods.

Findings

Approximately doubles accuracy on bias-prone sentences

Maintains overall F1 score of the classifier

Outperforms supervised masking approach in bias reduction

Abstract

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.