论文标题

部分可观测时空混沌系统的无模型预测

ReDDIT: Regret Detection and Domain Identification from Text

论文作者

Balouchzahi, Fazlourrahman, Butt, Sabur, Sidorov, Grigori, Gelbukh, Alexander

论文摘要

在本文中,我们提出了一项关于遗憾及其在社交媒体平台上的表达的研究。具体来说,我们介绍了一个新颖的Reddit文本数据集,这些数据集已分为三类:通过行动遗憾,无所作为后悔,也不后悔。然后,我们使用此数据集研究用于对Reddit表示遗憾的语言,并确定最常见的文本域。我们的发现表明,Reddit用户最有可能对过去的行动表示遗憾,尤其是在关系领域。我们还发现,在所有实验中,使用手套嵌入的其他模型的深度学习模型表明手套在遗憾领域中表示单词的含义和上下文的有效性。总体而言,我们的研究为社交媒体上遗憾的性质和流行提供了宝贵的见解,以及深度学习和单词嵌入的潜力,以分析和理解在线文本中的情感语言。这些发现对自然语言处理算法的发展以及支持情感表达和交流的社交媒体平台的设计具有影响。

In this paper, we present a study of regret and its expression on social media platforms. Specifically, we present a novel dataset of Reddit texts that have been classified into three classes: Regret by Action, Regret by Inaction, and No Regret. We then use this dataset to investigate the language used to express regret on Reddit and to identify the domains of text that are most commonly associated with regret. Our findings show that Reddit users are most likely to express regret for past actions, particularly in the domain of relationships. We also found that deep learning models using GloVe embedding outperformed other models in all experiments, indicating the effectiveness of GloVe for representing the meaning and context of words in the domain of regret. Overall, our study provides valuable insights into the nature and prevalence of regret on social media, as well as the potential of deep learning and word embeddings for analyzing and understanding emotional language in online text. These findings have implications for the development of natural language processing algorithms and the design of social media platforms that support emotional expression and communication.

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