ty -jour a2 -Versaci，Mario au -Sundararajan，Karthik au -Palanisamy，Anandhakumar PY -2020 DA -2020/01/09 TI-基于多元子的基于多元子的集合特征选择模型，用于Twitter SP -2860479 VL-2860479 VL-2860479 VL -2020AB-情感分析旨在推断人们如何对任何感兴趣的文本或主题表达意见。本文涉及对情感的隐性形式的检测，称为讽刺。讽刺与人们试图传达的方式相反，以便以幽默的方式批评或嘲笑。它在社交网络中起着至关重要的作用，因为大多数推文或帖子都包含讽刺的细微差别。现有的研究讽刺方法仅处理讽刺的检测。在本文中，除了从文本中检测讽刺外，还提出了一种方法来识别讽刺的类型。确定讽刺类型的主要动机是确定讽刺文本背后的伤害水平或真正的意图。拟议的工作旨在通过纳入一种新的观点来改善现有方法，该观点根据所采用的苛刻程度对讽刺进行分类。拟议作品的主要应用是将一个人的情绪状态与他/她所表现出的讽刺类型联系起来，这可以提供有关一个人的情感行为的主要见解。 An ensemble-based feature selection method has been proposed for identifying the optimal set of features needed to detect sarcasm from tweets. This optimal set of features was employed to detect whether the tweet is sarcastic or not. After detecting sarcastic sentences, a multi-rule based approach has been proposed to determine the type of sarcasm. As an initial attempt, sarcasm has been classified into four types, namely, polite sarcasm, rude sarcasm, raging sarcasm, and deadpan sarcasm. The performance and efficiency of the proposed approach has been experimentally analyzed, and change in mood of a person for each sarcastic type has been modelled. The overall accuracy of the proposed ensemble feature selection algorithm for sarcasm detection is around 92.7%, and the proposed multi-rule approach for sarcastic type identification achieves an accuracy of 95.98%, 96.20%, 99.79%, and 86.61% for polite, rude, raging, and deadpan types of sarcasm, respectively. SN - 1687-5265 UR - https://doi.org/10.1155/2020/2860479 DO - 10.1155/2020/2860479 JF - Computational Intelligence and Neuroscience PB - Hindawi KW - ER -