Neural Network Approach for Irony Detection from Arabic Text on Social Media Ali Allaith 1 , Muhammad Shahbaz 1 , Mohammed Alkoli 2 1 University of Engineering and Technology, Lahore, Pakistan 2 University of Mysore, Mysore, India allaith.net@gmail.com, m.shahbaz@uet.edu.pk, mohd.alkoli@gmail.com Abstract. Irony plays an important part in human social interaction which is used to emphasize occurrences that deviate from the expected. Humans manipulate each other in a very negative way by writing the opposite of what they mean. However, irony detection is a complex task even for humans. In this research, we study the problem of irony detection as a classification problem and utilized the dataset offered by the IDAT workshop. We also propose a classification system for detecting irony in the Arabic tweets using neural networks 1 . Keywords: Irony Detection, Text Classification, Neural Networks, Arabic Lan- guage. 1 Introduction The increasing of the information on the internet, especially in social media, leads to many natural language problems. People express their textual opinion in online re- sources like public forums and microblogging sites. One of the ways to express our opinion, which is the most interesting, is by using figurative languages such as irony and sarcasm. While there is a gap between the intended meaning and the literal mean- ing, irony detection becomes a very difficult task due to the ambiguous interpretations. Many researches and shared tasks related to the irony detection have been focused on different languages such as English, French, and Italian languages however, a little con- tribution has been done in Arabic language. Irony detecting has its implications in sentiment analysis, opinion mining, and adver- tising as shown in [1,2,3] respectively. For example, the detection of irony before ap- plying sentiment analysis is a big challenge where sometimes the presence of irony content may reverse the sentiment polarity of the text from positive to negative and vice versa. Therefore, the sentiment analysis systems which exploit the basic approaches of 1 "Copyright © 2019 for this paper by its authors. Use permitted under Creative Com- mons License Attribution 4.0 International (CC BY 4.0). FIRE 2019, 12-15 Decem- ber 2019, Kolkata, India."