This paper explores the task of identifying the overall sentiment expressed towards volitional entities (persons and organizations) in a document -- what we refer to as Entity-Level Sentiment Analysis (ELSA). While identifying sentiment conveyed towards an entity is well researched for shorter texts like tweets, we find little to no research on this specific task for longer texts with multiple mentions and opinions towards the same entity. This lack of research would be understandable if ELSA can be derived from existing tasks and models. To assess this, we annotate a set of professional reviews for their overall sentiment towards each volitional entity in the text. We sample from data already annotated for document-level, sentence-level, and target-level sentiment in a multi-domain review corpus, and our results indicate that there is no single proxy task that provides this overall sentiment we seek for the entities at a satisfactory level of performance. We present a suite of experiments aiming to assess the contribution towards ELSA provided by document-, sentence-, and target-level sentiment analysis, and provide a discussion of their shortcomings. We show that sentiment in our dataset is expressed not only with an entity mention as target, but also towards targets with a sentiment-relevant relation to a volitional entity. In our data, these relations extend beyond anaphoric coreference resolution, and our findings call for further research of the topic. Finally, we also present a survey of previous relevant work.

翻译：本文探讨了在文档中识别针对意志实体（个人和组织）的整体情感倾向任务——我们称之为实体级情感分析（ELSA）。尽管针对推文等短文本中实体情感识别的研究已较为成熟，但对于包含同一实体多次提及和多种观点的长文本，我们发现几乎没有针对这一特定任务的研究。如果ELSA能从现有任务和模型中推导出来，这种研究缺失尚可理解。为验证此假设，我们标注了一组专业评论，评估文本中每个意志实体的整体情感倾向。我们从已标注文档级、句子级和目标级情感的多领域评论文本中抽样数据，结果表明，没有任何单一代理任务能完美提供我们所需的实体整体情感。我们开展了一系列实验，评估文档级、句子级和目标级情感分析对ELSA的贡献，并讨论了它们的局限性。研究发现，数据集中情感表达不仅以实体提及为目标，还针对与意志实体具有情感相关关系的目标。在我们的数据中，这些关系超越了回指共指消解范畴，研究结果呼吁对该主题进行进一步探索。最后，我们还对以往相关研究进行了综述。