Weakly Supervised Attention Networks for Fine-Grained Opinion Mining and Public Health
This work addresses the need for fine-grained analysis in review classification and public health applications, offering a weakly supervised method that reduces labeling costs.
The paper tackled the problem of fine-grained opinion mining using only review-level labels by proposing a new Multiple Instance Learning aggregation function based on sigmoid attention, which improved segment-level sentiment classification F1 by up to 9.8% and increased recall for foodborne illness reports by 48.6%.
In many review classification applications, a fine-grained analysis of the reviews is desirable, because different segments (e.g., sentences) of a review may focus on different aspects of the entity in question. However, training supervised models for segment-level classification requires segment labels, which may be more difficult or expensive to obtain than review labels. In this paper, we employ Multiple Instance Learning (MIL) and use only weak supervision in the form of a single label per review. First, we show that when inappropriate MIL aggregation functions are used, then MIL-based networks are outperformed by simpler baselines. Second, we propose a new aggregation function based on the sigmoid attention mechanism and show that our proposed model outperforms the state-of-the-art models for segment-level sentiment classification (by up to 9.8% in F1). Finally, we highlight the importance of fine-grained predictions in an important public-health application: finding actionable reports of foodborne illness. We show that our model achieves 48.6% higher recall compared to previous models, thus increasing the chance of identifying previously unknown foodborne outbreaks.