  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.
