  Interactive sentiment analysis is an emerging, yet challenging, subtask of
the sentiment analysis problem. It aims to discover the affective state and
sentimental change of each person in a conversation. Existing sentiment
analysis approaches are insufficient in modelling the interactions among
people. However, the development of new approaches are critically limited by
the lack of labelled interactive sentiment datasets. In this paper, we present
a new conversational emotion database that we have created and made publically
available, namely ScenarioSA. We manually label 2,214 multi-turn English
conversations collected from natural contexts. In comparison with existing
sentiment datasets, ScenarioSA (1) covers a wide range of scenarios; (2)
describes the interactions between two speakers; and (3) reflects the
sentimental evolution of each speaker over the course of a conversation.
Finally, we evaluate various state-of-the-art algorithms on ScenarioSA,
demonstrating the need of novel interactive sentiment analysis models and the
potential of ScenarioSA to facilitate the development of such models.
