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 at
this https URL

本文提出一种 Three-hop Reasoning CoT Framework，用于处理隐含情感分析，它模拟了人类推理过程，利用多步提示逐步诱导潜在方面、意见和情感极性，并且在监督设置中的 F1 值超过了 6% 的 state-of-the-art。而在零 - shot 的情况下，THOR + GPT3 (175B) 将 state-of-the-art 的 F1 值提高了 50%。

利用思维链提示推理隐含情感

Reasoning Implicit Sentiment with Chain-of-Thought Prompting

Aspect-based sentiment analysis aims to identify the sentiment polarity of a
specific aspect in product reviews. We notice that about 30% of reviews do not
contain obvious opinion words, but still convey clear human-aware sentiment
orientation, which is known as implicit sentiment. However, recent neural
network-based approaches paid little attention to implicit sentiment entailed
in the reviews. To overcome this issue, we adopt Supervised Contrastive
Pre-training on large-scale sentiment-annotated corpora retrieved from
in-domain language resources. By aligning the representation of implicit
sentiment expressions to those with the same sentiment label, the pre-training
process leads to better capture of both implicit and explicit sentiment
orientation towards aspects in reviews. Experimental results show that our
method achieves state-of-the-art performance on SemEval2014 benchmarks, and
comprehensive analysis validates its effectiveness on learning implicit
sentiment.

采用超级对比预训练的方法，利用语言资源中的大规模情感注释语料库，对于产品评论中蕴含的明示和隐含情感表达进行预测，大幅提高对于细节方面表达情感的识别率，这种方法不仅在论文中实验证明了其有效性，还在 SemEval2014 基准测试集上取得了最好的表现。