  Fine-grained sentiment analysis attempts to extract sentiment holders,
targets and polar expressions and resolve the relationship between them, but
progress has been hampered by the difficulty of annotation. Targeted sentiment
analysis, on the other hand, is a more narrow task, focusing on extracting
sentiment targets and classifying their polarity.In this paper, we explore
whether incorporating holder and expression information can improve target
extraction and classification and perform experiments on eight English
datasets. We conclude that jointly predicting target and polarity BIO labels
improves target extraction, and that augmenting the input text with gold
expressions generally improves targeted polarity classification. This
highlights the potential importance of annotating expressions for fine-grained
sentiment datasets. At the same time, our results show that performance of
current models for predicting polar expressions is poor, hampering the benefit
of this information in practice.
