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.
翻译:精细的情绪分析试图提取情绪持有者、目标和极地表达方式,并解决它们之间的关系,但进展受到注释困难的阻碍。另一方面,定向情绪分析是一项更狭窄的任务,重点是提取情绪目标并分类其极性。 在本文中,我们探讨将持有者和表达信息纳入其中是否可以改进目标提取和分类,并对八个英国数据集进行实验。我们的结论是,联合预测目标和极地BIO标签可以改善目标提取,而用黄金表达方式增加输入文本通常可以改善目标极性分类。这凸显了微微微度情绪数据集的注释表达方式的潜在重要性。 同时,我们的结果显示,目前预测极地表达方式的模式表现不佳,妨碍了这一信息在实践中的效益。