Recent developments in the field of AI have fostered multidisciplinary research in various disciplines, including computer science, linguistics, and psychology. Intelligence, in fact, is much more than just IQ: it comprises many other kinds of intelligence, including physical intelligence, cultural intelligence, linguistic intelligence, and emotional intelligence (EQ). While traditional classification tasks and standard phenomena in computer science are easy to define, however, emotions are still a rather mysterious subject of study. That is why so many different emotion classifications have been proposed in the literature and there is still no common agreement on a universal emotion categorization model. In this article, we revisit the Hourglass of Emotions, an emotion categorization model optimized for polarity detection, based on some recent empirical evidence in the context of sentiment analysis. This new model does not claim to offer the ultimate emotion categorization but it proves the most effective for the task of sentiment analysis.

中文翻译：

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重新审视沙漏模型

人工智能领域的最新发展促进了各学科的多学科研究，包括计算机科学、语言学和心理学。事实上，智力不仅仅是智商：它还包括许多其他类型的智力，包括身体智力、文化智力、语言智力和情商 (EQ)。尽管计算机科学中的传统分类任务和标准现象很容易定义，但情感仍然是一个相当神秘的研究课题。这就是为什么在文献中提出了这么多不同的情绪分类，而对于通用的情绪分类模型仍然没有达成共识的原因。在本文中，我们重新审视了情绪沙漏，一种针对极性检测优化的情绪分类模型，基于最近在情感分析背景下的一些经验证据。这种新模型并没有声称提供最终的情感分类，但它被证明对情感分析任务最有效。