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Fine-grained sentiment analysis attempts to extract sentiment holders,targets and polar expressions and resolve the relationship between them, butprogress has been hampered by the difficulty of annotation. Targeted sentimentanalysis, on the other hand, is a more narrow task, focusing on extractingsentiment targets and classifying their polarity.In this paper, we explorewhether incorporating holder and expression information can improve targetextraction and classification and perform experiments on eight Englishdatasets. We conclude that jointly predicting target and polarity BIO labelsimproves target extraction, and that augmenting the input text with goldexpressions generally improves targeted polarity classification. Thishighlights the potential importance of annotating expressions for fine-grainedsentiment datasets. At the same time, our results show that performance ofcurrent models for predicting polar expressions is poor, hampering the benefitof this information in practice.

If you've got it, flaunt it: Making the most of fine-grained sentiment annotations