Open-Domain Targeted Sentiment Analysisvia Span-Based Extraction and Classification
Abstract
Open-domain targeted sentiment analysis aims
to detect opinion targets along with their sentiment polarities from a sentence. Prior work
typically formulates this task as a sequence
tagging problem. However, such formulation
suffers from problems such as huge search
space and sentiment inconsistency. To address these problems, we propose a span-based
extract-then-classify framework, where multiple opinion targets are directly extracted from
the sentence under the supervision of target
span boundaries, and corresponding polarities
are then classified using their span representations. We further investigate three approaches
under this framework, namely the pipeline,
joint, and collapsed models. Experiments on
three benchmark datasets show that our approach consistently outperforms the sequence
tagging baseline. Moreover, we find that the
pipeline model achieves the best performance
compared with the other two models.