Department of Information Technology, Madras Institute of Technology, Anna University, Chennai-603202, Tamilnadu, India.
Comput Intell Neurosci. 2020 Jan 9;2020:2860479. doi: 10.1155/2020/2860479. eCollection 2020.
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.
情感分析旨在推断人们如何表达对任何文本或感兴趣主题的意见。本文涉及到一种情感的隐式形式的检测，称为讽刺。讽刺表达的是人们试图传达的相反的意思，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。现有的讽刺研究方法只涉及讽刺的检测。在本文中，除了从文本中检测讽刺外，还提出了一种从文本中识别讽刺类型的方法。确定讽刺类型的主要动机是识别讽刺文本背后的伤害程度或真实意图。这项工作旨在通过引入一种新的视角来改进现有的方法，根据使用的严厉程度对讽刺进行分类。这项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联系起来，这可以提供关于一个人情绪行为的主要见解。本文提出了一种基于集成的特征选择方法，用于识别从推文中检测讽刺所需的最佳特征集。该最佳特征集用于检测推文是否为讽刺。在检测到讽刺句子后，提出了一种基于多规则的方法来确定讽刺的类型。作为初步尝试，将讽刺分为四种类型，即礼貌讽刺、粗鲁讽刺、愤怒讽刺和无表情讽刺。实验分析了所提出方法的性能和效率，并对每种讽刺类型的人的情绪变化进行了建模。所提出的集成特征选择算法对讽刺检测的整体准确性约为 92.7%，而所提出的多规则方法对讽刺类型识别的准确性分别为 95.98%、96.20%、99.79%和 86.61%，用于礼貌、粗鲁、愤怒和无表情的讽刺类型。
