Abstract
Sentiment analysis (SA) aims to identify the sentiment expressed in a text, such as a product review. Given a review and the sentiment associated with it, this paper formulates SA as a combination of two tasks: (1) a causal discovery task that distinguishes whether a review "primes" the sentiment (Causal Hypothesis C1), or the sentiment "primes" the review (Causal Hypothesis C2); and (2) the traditional prediction task to model the sentiment using the review as input. Using the peak-end rule in psychology, we classify a sample as C1 if its overall sentiment score approximates an average of all the sentence-level sentiments in the review, and C2 if the overall sentiment score approximates an average of the peak and end sentiments. For the prediction task, we use the discovered causal mechanisms behind the samples to improve the performance of LLMs by proposing causal prompts that give the models an inductive bias of the underlying causal graph, leading to substantial improvements by up to 32.13 F1 points on zero-shot five-class SA. Our code is at this https URL
Abstract (translated)
情感分析（SA）旨在识别文本中表达的情感，如产品评论。给定一个评论及其情感，本文将SA分解为两个任务：（1）一个因果发现任务，区分是否是评论“推动了”情感（因果假设C1），或者是情感“推动了”评论（因果假设C2）；和（2）传统预测任务，使用评论作为输入来建模情感。根据心理学的峰值-端规则，我们将样本分类为C1，如果其整体情感得分约等于评论中所有句子级情感的平均值，则C1；如果整体情感得分约等于评论中峰值和结束情感的平均值，则C2。对于预测任务，我们使用发现样本背后的因果机制来提高LLM的性能，通过提出因果提示，使模型对潜在因果图具有归纳偏见，从而在零散五类SA上实现显著的改进，和改进后的F1得分达到32.13。我们的代码位于此链接：
URL
https://arxiv.org/abs/2404.11055
