  While sentiment analysis systems try to determine the sentiment polarities of
given targets based on the key opinion expressions in input texts, in implicit
sentiment analysis (ISA) the opinion cues come in an implicit and obscure
manner. Thus detecting implicit sentiment requires the common-sense and
multi-hop reasoning ability to infer the latent intent of opinion. Inspired by
the recent chain-of-thought (CoT) idea, in this work we introduce a Three-hop
Reasoning (THOR) CoT framework to mimic the human-like reasoning process for
ISA. We design a three-step prompting principle for THOR to step-by-step induce
the implicit aspect, opinion, and finally the sentiment polarity. Our
THOR+Flan-T5 (11B) pushes the state-of-the-art (SoTA) by over 6% F1 on
supervised setup. More strikingly, THOR+GPT3 (175B) boosts the SoTA by over 50%
F1 on zero-shot setting. Our code is open at
https://github.com/scofield7419/THOR-ISA.
