TY -的A2 Versaci马里奥盟——Sundararajan Karthik盟——Palanisamy Anandhakumar PY - 2020 DA - 2020/01/09 TI -多基于整体特征选择模型为讽刺类型检测在Twitter SP - 2860479六世- 2020 AB -情感分析旨在推断人们表达他们的意见在任何一段文本或感兴趣的话题。本文处理检测隐式形式的情绪,称为讽刺。讽刺传达相反的人们试图传达幽默的方式为了批评或嘲笑。它在社交网络中发挥着至关重要的作用,因为大多数的tweet或者帖子含有讽刺的细微差别。现有方法对研究讽刺只处理讽刺的检测。本文除了讽刺从文本检测,提出了一种方法来识别类型的讽刺。确定类型的讽刺背后的主要动机是确定水平的伤害或讽刺文本背后的真正意图。拟议的工作旨在改进现有方法中加入一个新的视角,将残酷的讽刺基于水平。该工作的主要应用是一个人的情绪状态有关的类型讽刺,他/她可以提供关于一个人的情感行为的主要见解。一个ensemble-based特征选择方法提出了确定最优的功能需要检测讽刺tweet。 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 -