情感分析需要亚文化吗？一种新的数据增强方法
Will sentiment analysis need subculture? A new data augmentation approach
AI总结：
针对情感分析训练数据不足的问题，提出基于亚文化的数据增强方法SCDA，通过构建特定亚文化表达生成器生成增强文本，实验验证其有效性并发现亚文化表达的情感刺激差异及线性可逆性猜想。
摘要：
如今，互联网的无处不在催生了一种围绕当代环境聚集的亚文化。这种亚文化通过热切追求新奇的吸引力，巧妙地表达了人类情感的复杂性，这一事实在情感分析中不容忽视。本文旨在透过亚文化的视角丰富数据，以解决情感分析面临的训练数据不足问题。为此，提出了一种基于亚文化的数据增强（SCDA）新方法，该方法通过利用特定亚文化表达生成器的创建，为每个训练文本生成增强文本。大量实验证明了SCDA的有效性和潜力。结果还揭示了不同的亚文化表达会引发不同程度情感刺激的现象。此外，还产生了一个有趣的猜想，表明某些亚文化表达具有线性可逆性。
英文摘要：
Nowadays, the omnipresence of the Internet has fostered a subculture that congregates around the contemporary milieu. The subculture artfully articulates the intricacies of human feelings by ardently pursuing the allure of novelty, a fact that cannot be disregarded in the sentiment analysis. This paper aims to enrich data through the lens of subculture, to address the insufficient training data faced by sentiment analysis. To this end, a new approach of subculture-based data augmentation (SCDA) is proposed, which engenders enhanced texts for each training text by leveraging the creation of specific subcultural expression generators. The extensive experiments attest to the effectiveness and potential of SCDA. The results also shed light on the phenomenon that disparate subcultural expressions elicit varying degrees of sentiment stimulation. Moreover, an intriguing conjecture arises, suggesting the linear reversibility of certain subcultural expressions.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
