Challenges of Sarcasm Detection for Social Network : A Literature Review

Nowadays, sarcasm recognition and detection simplified with various domains knowledge, among others, computer science, social science, psychology, mathematics, and many more. This article aims to explain trends in sentiment analysis especially sarcasm detection in the last ten years and its direction in the future. We review journals with the title’s keyword “sarcasm” and published from the year 2008 until 2018. The articles were classified based on the most frequently discussed topics among others: the dataset, pre-processing, annotations, approaches, features, context, and methods used. The significant increase in the number of articles on “sarcasm” in recent years indicates that research in this area still has enormous opportunities. The research about “sarcasm” also became very interesting because only a few researchers offer solutions for unstructured language. Some hybrid approaches using classification and feature extraction are used to identify the sarcasm sentence using deep learning models. This article will provide a further explanation of the most widely used algorithms for sarcasm detection with object social media. At the end of this article also shown that the critical aspect of research on sarcasm sentence that could be done in the future is dataset usage with various languages that cover unstructured data problem with contextual information will effectively detect sarcasm sentence and will improve the existing performance

Detection of Sarcasm and Nastiness: New Resources for Spanish Language

The main goal of this work is to provide the cognitive computing community with valuable resources to analyze and simulate the intentionality and/or emotions embedded in the language employed in social media. Specifically, it is focused on the Spanish language and online dialogues, leading to the creation of SOFOCO (Spanish Online Forums Corpus). It is the first Spanish corpus consisting of dialogic debates extracted from social media and it is annotated by means of crowdsourcing in order to carry out automatic analysis of subjective language forms, like sarcasm or nastiness. Furthermore, the annotators were also asked about the context need when taking a decision. In this way, the users’ intentions and their behavior inside social networks can be better understood and more accurate text analysis is possible. An analysis of the annotation results is carried out and the reliability of the annotations is also explored. Additionally, sarcasm and nastiness detection results (around 0.76 F-Measure in both cases) are also reported. The obtained results show the presented corpus as a valuable resource that might be used in very diverse future work.This study was partially funded by the Spanish Government (TIN2014-54288-C4-4-R and TIN2017-85854-C4-3-R) by the European Unions’s H2020 program under grant 769872 and by the National Science Foundation of USA (NSF CISE R1 #1202668