  In sentiment analysis of longer texts, there may be a variety of topics
discussed, of entities mentioned, and of sentiments expressed regarding each
entity. We find a lack of studies exploring how such texts express their
sentiment towards each entity of interest, and how these sentiments can be
modelled. In order to better understand how sentiment regarding persons and
organizations (each entity in our scope) is expressed in longer texts, we have
collected a dataset of expert annotations where the overall sentiment regarding
each entity is identified, together with the sentence-level sentiment for these
entities separately. We show that the reader's perceived sentiment regarding an
entity often differs from an arithmetic aggregation of sentiments at the
sentence level. Only 70\% of the positive and 55\% of the negative entities
receive a correct overall sentiment label when we aggregate the
(human-annotated) sentiment labels for the sentences where the entity is
mentioned. Our dataset reveals the complexity of entity-specific sentiment in
longer texts, and allows for more precise modelling and evaluation of such
sentiment expressions.
