  Recently, ChatGPT has drawn great attention from both the research community
and the public. We are particularly interested in whether it can serve as a
universal sentiment analyzer. To this end, in this work, we provide a
preliminary evaluation of ChatGPT on the understanding of \emph{opinions},
\emph{sentiments}, and \emph{emotions} contained in the text. Specifically, we
evaluate it in three settings, including \emph{standard} evaluation,
\emph{polarity shift} evaluation and \emph{open-domain} evaluation. We conduct
an evaluation on 7 representative sentiment analysis tasks covering 17
benchmark datasets and compare ChatGPT with fine-tuned BERT and corresponding
state-of-the-art (SOTA) models on them. We also attempt several popular
prompting techniques to elicit the ability further. Moreover, we conduct human
evaluation and present some qualitative case studies to gain a deep
comprehension of its sentiment analysis capabilities.
