Sentimental analysis aims at inferring how people express their opinion over any piece of text or topic of interest. This article deals with detection of an implicit form of the sentiment, referred to as sarcasm. Sarcasm conveys the opposite of what people try to convey in order to criticize or ridicule in a humorous way. It plays a vital role in social networks since most of the tweets or posts contain sarcastic nuances. Existing approaches towards the study of sarcasm deals only with the detection of sarcasm. In this paper, in addition to detecting sarcasm from text, an approach has been proposed to identify the type of sarcasm. The main motivation behind determining the types of sarcasm is to identify the level of hurt or the true intent behind the sarcastic text. The proposed work aims to improve upon the existing approaches by incorporating a new perspective which classifies the sarcasm based on the level of harshness employed. The major application of the proposed work would be relating the emotional state of a person to the type of sarcasm exhibited by him/her which could provide major insights about the emotional behavior of a person. 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.

中文翻译：

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基于多规则的集成特征选择模型，用于Twitter中的Sarcasm类型检测。

情感分析旨在推断人们如何表达对任何感兴趣的文本或主题的看法。本文介绍了对情感的一种隐式形式的发现，即讽刺。讽刺传达了人们试图以幽默的方式批评或嘲笑的内容。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性的细微差别。现有的嘲讽研究方法仅涉及嘲讽的检测。在本文中，除了从文本中检测出讽刺外，还提出了一种识别讽刺类型的方法。确定讽刺类型背后的主要动机是确定讽刺文字背后的伤害程度或真实意图。拟议的工作旨在通过结合新观点来改进现有方法，该观点根据所采用的苛刻程度对讽刺进行分类。拟议工作的主要应用是将一个人的情绪状态与他/她表现出的讽刺类型相关联，这可以提供有关一个人的情绪行为的重要见解。已经提出了一种基于整体的特征选择方法，用于识别从推文中检测讽刺所需的最佳特征集。使用此最佳功能集来检测该推文是否具有讽刺意味。在检测到讽刺句之后，提出了一种基于多规则的方法来确定讽刺的类型。作为一种初步尝试，讽刺被分为四种类型，即礼貌的讽刺，粗鲁的讽刺，愤怒的讽刺，和无聊的嘲讽。通过实验分析了所提出方法的性能和效率，并且对每种讽刺类型的人的情绪变化进行了建模。提出的用于讽刺检测的整体特征选择算法的整体准确度约为92.7％，并且提出的讽刺类型识别的多规则方法对于礼貌，粗鲁，粗暴，不礼貌的人，可以达到95.98％，96.20％，99.79％和86.61％的准确性。怒骂和傻瓜型的嘲讽分别。