Idioms represent a ubiquitous vehicle for conveying sentiments in the realm of everyday discourse, rendering the nuanced analysis of idiom sentiment crucial for a comprehensive understanding of emotional expression within real-world texts. Nevertheless, the existing corpora dedicated to idiom sentiment analysis considerably limit research in text sentiment analysis. In this paper, we propose an innovative approach to automatically expand the sentiment lexicon for idioms, leveraging the capabilities of large language models through the application of Chain-of-Thought prompting. To demonstrate the effectiveness of this approach, we integrate multiple existing resources and construct an emotional idiom lexicon expansion dataset (called EmoIdiomE), which encompasses a comprehensive repository of Chinese and English idioms. Then we designed the Dual Chain-of-Thoughts (DualCoTs) method, which combines insights from linguistics and psycholinguistics, to demonstrate the effectiveness of using large models to automatically expand the sentiment lexicon for idioms. Experiments show that DualCoTs is effective in idioms sentiment lexicon expansion in both Chinese and English. For reproducibility, we will release the data and code upon acceptance.

翻译：成语在日常话语领域中普遍用于传达情感，因此对成语情感的细致分析对于全面理解真实文本中的情感表达至关重要。然而，现有专门用于成语情感分析的语料库极大地限制了文本情感分析的研究。本文提出了一种创新方法，通过应用思维链提示技术，利用大语言模型的能力，自动扩展成语的情感词典。为验证该方法的有效性，我们整合了多个现有资源，构建了一个情感成语扩展数据集（称为EmoIdiomE），其中包含全面的中英文成语库。随后，我们设计了双重思维链方法，该方法结合了语言学和心理语言学的洞见，以证明使用大模型自动扩展成语情感词典的有效性。实验表明，DualCoTs在中文和英文的成语情感词典扩展中均表现出有效性。为确保可复现性，我们将在论文被接受后公开数据和代码。