  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.
