Abstract
A considerable amount of research has been de-veloped lately to analyze social media with the intention of understanding and exploiting the available information. Recently, irony has took a significant role in human communication as it has been increasingly used in many social media platforms. In Natural Language Processing (NLP), irony recognition is an important yet difficult problem to solve. It is considered to be a complex linguistic phenomenon in which people means the opposite of what they literally say. Due to its significance, it becomes essential to analyze and detect irony in subjective texts to improve the analysis tools to classify people opinion automatically. This paper explores how deep learning methods can be employed to the detection of irony in Arabic language with the help of Word2vec term representations that converts words to vectors. We applied two different deep learning models; Convolutional Neural Network (CNN) and Bidirectional Long Short-Term Memory (BiLSTM). We tested our frameworks with a manually annotated datasets that was collected using Tweet Scraper. The best result was achieved by the CNN model with an F1 score of 0.87.
Highlights
Sentiment analysis is known as the extraction and interpretation of opinions expressed in a text written in a natural language on a certain subject [1]
Potamias et al [14] proposed a transformer based architecture that builds on the pre-trained RoBERTa model and integrated with a recurrent convolutional neural network (RCNN) that uses nonhand crafted features as they argue that overly trained deep learning approach does not need engineered feature step
We experimented with Bidirectional Long Short-Term Memory (BiLSTM) and reached 0.86 F1 score, while the results achieved by [12] using BiLSTM model is 0.83
Summary
Sentiment analysis is known as the extraction and interpretation of opinions expressed in a text written in a natural language on a certain subject [1]. Irony has been studied by many research fields such as psychology [8], linguistics education [9], and computational science It is used widely as an indirect negation in order to achieve different communication goals in many situations such as criticizing, make fun of people, and manipulate answers to upsetting questions. The appearance of irony in social networks such as microblogs has greatly increased For this reason, one of the primary motivations behind this research is detecting real intention behind posts accurately and understanding how people feel regarding specific matters can be useful for many applications. One of the primary motivations behind this research is detecting real intention behind posts accurately and understanding how people feel regarding specific matters can be useful for many applications It can help in correctly identifying security issues such as threatening posts by verifying whether the threat words are literal or not. This paper is structured as follows: Section 2 presents a brief literature review on irony detection; Section 3 discusses the approach including data generation process, feature extraction, and the proposed method; Section 4 dedicated to the results; lastly, Section 5 concludes the paper
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