Sarcasm is a sophisticated phenomenon used for conveying a meaning that differs from what is being said, and it is usually used to express displeasure or ridicule others. Sentiment analysis is a process of uncovering the subjective information from a text. Detecting figurative language such as irony or sarcasm, is a focused challenging research field of sentiment analysis. Detecting and understanding the use of sarcasm in social networks could provide businesses and politicians with significant insight, since it reflects people's opinions about certain topics, news, and products. This has especially become relevant recently because sarcastic texts have been trending on social networks and are being posted by millions of active users. As a result of this situation, there is now an increasing amount of research on the detection of sarcasm in social network posts. Many works have been published on sarcasm detection, and they include a wide variety of techniques based on rules, lexicons, traditional machine learning, deep learning, and transformers. However, sarcasm detection is a challenging task due to the ambiguity and non-straightforward nature of sarcastic text. In addition, very few reviews have been conducted on the research in this area. Therefore, this systematic review mainly aims at exploring the newly published sarcasm detection articles on social networks in the years between 2019 and 2022. Several databases were extensively searched, and 30 articles that met the criteria were included. The selected articles were reviewed based on their approaches, datasets, and evaluation metrics. The findings emphasized that deep learning is the most commonly used technique for sarcasm detection in recent literature, and Twitter and F-measure are the most used source and performance metric, respectively. Finally, this article presents a brief discussion regarding the challenges in sarcasm detection and future research directions.
The unsupervised morphology processing in the emerging mutant languages has the advantage over the human/ supervised processing of being more agiler. The main drawback is, however, their accuracy. This article describes an unsupervised morphemes identification approach based on an intuitive and formal definition of event dependence. The input is no more than a plain text of the targeted language. Although the original objective of this work was classical Arabic, the test was conducted on an English set as well. Tests on these two languages show a very acceptable precision and recall. A deeper refinement of the output allowed 89% precision and 78% recall on Arabic.
Abstract-Assessment of the similarities between texts has been studied for decades from different perspectives and for several purposes. One interesting perspective is the morphology. This article reports the results on a study on the assessment of the morphological relatedness between natural language words. The main idea is to adapt a formal string alignment algorithm namely Needleman-Wunsch's to accommodate the statistical characteristics of the words in order to approximate how similar are the linguistic morphologies of the two words. The approach is unsupervised from end to end and the experiments show an nDCG reaching 87% and an r-precision reaching 81%.
Abstract-Virtually all of today's organizations store their data in huge databases to retrieve, manipulate and share them in an efficient way. Due to the popularity of databases for storing important and critical data, they are becoming subject to an overwhelming range of threats, such as unauthorized access. Such a threat can result in severe financial or privacy problems, as well as other corruptions. To tackle possible threats, numerous security mechanisms have emerged to protect data housed in databases. Among the most successful database security mechanisms is database encryption. This has the potential to secure the data at rest by converting the data into a form that cannot be easily understood by unauthorized persons. Many encryption algorithms have been proposed, such as Transposition-Substitution-Folding-Shifting encryption algorithm (TSFS), Data Encryption Standard (DES), and Advanced Encryption Standard (AES) algorithms. Each algorithm has advantages and disadvantages, leaving room for optimization in different ways. This paper proposes enhancing the TSFS algorithm by extending its data set to special characters, as well as correcting its substitution and shifting steps to avoid the errors occurring during the decryption process. Experimental results demonstrate the superiority of the proposed algorithm, as it has outperformed the well-established benchmark algorithms, DES and AES, in terms of query execution time and database added size.
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