A fuzzy approach for sarcasm detection in social networks

The great success of social networks is due to their ability to offer Internet users a space of free expression where they can produce a large amount of information which provides every day with a new challenge for data analysts. The ease of use of social media encourages users to increasingly express their opinions either by using simple words expressing feelings or by using irony and sarcasm. The new challenges are to extract and analyze this mass of information which can then be used in different applications such as sentiment analysis and sarcasm detection. Sarcasm detection is a subarea of sentiment analysis, opinion mining, and emotion mining which are all representing the process of automatic identification of people's orientation or sentiment toward individuals, products, services, issues, and events. Sarcasm detection, which is the fact of deciding if a text is ironic or not, could be used, for example, to improve the precision of the sentiment analysis. In most of the existing approaches, sarcasm detection is a binary classification; each text is classified as sarcastic or non-sarcastic; however, since tweets are generally written by humans and humans are by default fuzzy in their emotions and expressions, we can't 100% confirm that a text is sarcastic or not. In addition, tweets are expressed in natural language which is full of ambiguity and non-precision, which motivates us more to adopt fuzzy logic, not just to detect sarcasm but to give it a score. In this manuscript, we propose a fuzzy sarcasm detection approach using social information such as replies, historical tweets and likes, etc multiplying each by a degree of importance. The evaluation shows that the use of fuzzy logic has led us to improve the precision metric of the classification and to improve the accuracy of our approach. Using degrees of importance gave us the best values for recall, precision, and accuracy measures compared to existing approaches. (C) 2021 The Authors. Published by Elsevier B.V. This is an open access article under the CC BY-NC-ND license (https://crativecommons.org/licenses/by-nc-nd/4.0) Peer-review under responsibility of the scientific committee of KES International.