Sattam Almatarneh

Zarqa University · Artificial intelligence

A specific type of virus called Systems and Resources ransomware attacks, which prevents users from accessing these systems and resources until a ransom is paid. This attack is based on file encryption and/or computer locking, it achieves this by denial of service to its user on the system. Malware exploits people's fear of exposing their critical...

Phishing is a cybercrime that is constantly increasing in the recent years due to the increased use of the Internet and its applications. It is one of the most common types of social engineering that aims to disclose or steel users sensitive or personal information. In this paper, two main objectives are considered. The first is to identify the bes...

Associative classification (AC) has been shown to outperform other methods of single-label classification for over 20 years. In order to create rules that are both more precise and simpler to grasp, AC combines the rules of mining associations with the task of classification. However, the current state of knowledge and the views of various speciali...

Summarization systems are needed to handle the vast amounts of text data being collected by machine-controlled systems. These systems help users to get a better understanding of the main concepts of the paper. This type of summary is produced by people who produce extract summaries. It involves gathering significant excerpts from a text and not cre...

Wireless Sensor Networks are becoming more prevalent in various industries, including military operations and distant environmental monitoring. This is important because sensors are getting smarter, smaller, and less expensive. The energy hole problem in the WSN has been a major focus of recent research. The mobile sink is an efficient solution for...

Crowdfunding is important for backing innovative projects and new startup businesses. However, success in achieving the target fundraising is a big challenge, and it depends on many complex factors. This work uses data science to predict the success of crowdfunding pledges using a historical dataset that was scrapped from the Kickstarter website. T...

Consistently with social and political concern about hatred and harassment through social media, in recent years, automatic hate-speech detection and offensive behavior in social media are gaining a lot of attention. In this paper, we examine the performance of several supervised classifiers in the process of identifying hate speech on Twitter. Mor...

This article describes a system that participated in the Bots and Gender Profiling shared task at PAN 2019. The first objective of the task is to detect whether the author of a Twitter account is a bot or a human; and in case of human, the second objective is to identify the gender of the user account. For this purpose, we present a Bayesian strate...

This paper is comparing a method to automatically build a sentiment lexicon, with four well-known sentiment lexicons. For this purpose, an indirect evaluation is carried out. The lexicons are integrated into supervised sentiment classifiers, and their performance is evaluated in two sentiment classification tasks to identify i) the most negative vs...

In the article we look at an architecture of a detector of groups of small objects in close proximity to each other with distances between them as short as couples of pixels. In modern days the issue with detection of such small objects using a neural network is often the pooling based architecture leading to spatial information loss. We suggest a...

This article describes the strategy submitted by the CiTIUS-COLE team to SemEval 2019 Task 5, a task which consists of binary classification where the system predicting whether a tweet in English or in Spanish is hateful against women or immigrants or not. The proposed strategy relies on combining linguistic features to improve the classifier’s per...

In this paper, we examine the performance of several classifiers in the process of searching for very negative opinions. More precisely, we do an empirical study that analyzes the influence of three types of linguistic features (n-grams, word embeddings, and polarity lexicons) and their combinations when they are used to feed different supervised m...

Studies in sentiment analysis and opinion mining focused on many aspects related to opin- ions, particularly polarity classification by making use of positive, negative or neutral values. However, most studies overlooked the identification of extreme opinions (very negative and very positive opinions) in spite of their vast significance in many app...

Studies in sentiment analysis and opinion mining have examined how different features are effective in polarity classification by making use of positive, negative or neutral values. However, the identification of extreme opinions (most negative and most positive opinions) have overlooked in spite of their wide significance in many applications. In...

This file contains two lexicons at two different borderline (B) values: B=1 and B=2. Each of our two lexicons, VERY-NEG and VERY-POS: 1- VERY-NEG which consists of a list of the most negative words (MN) and words that are considered to be not most negative (NMN). 2- VERY-POS is composed of a list of the most positive words (MP) and of those words...

SPLM is a ranked opinion lexicon. We obtained the weights assigned to each word-tag pair by making use of the equations defined on the following paper which describe all details about this lexicon. Format The file is tab delimited with: Word: the words. Tag: Part of speech (POS), - r (adverb) - a (adjective). Polarity: positive / negative. D(...

This paper is comparing a method to automatically build a sentiment lexicon, with four well-known sentiment lexicons. For this purpose, an indirect evaluation is carried out. The lexicons are integrated into supervised sentiment classifiers, and their performance is evaluated in two sentiment classification tasks to identify i) the most negative vs...

The article describes a strategy to build sentiment lexicons (positive and negative words) from corpora. Special attention will paid to the construction of a domain-specific lexicon from a corpus of movie reviews. Polarity words of the lexicon are assigned weights standing for different degrees of positiveness and negativeness. This lexicon is inte...

Studies in sentiment analysis and opinion mining have been focused on many aspects related to opinions, namely polarity classification by making use of positive, negative or neutral values. However, most studies have overlooked the identification of extreme opinions (most negative and most positive opinions) in spite of their vast significance in m...

Sentiment lexicons are considered the primary building block in sentiment analysis as it is an essential resource for most sentiment analysis algorithms, and the first indicator to express positive or negative opinions. This presentation will show new methods to build sentiment lexicons and how to use them to identify extreme opinions.

Studies in sentiment analysis and opinion mining have been focused on several aspects of opinions, such as their automatic extraction, identification of their polarity (positive, negative or neutral), the entities or facets involved, and so on. However, to the best of our knowledge, no sentiment analysis approach has considered the automatic identi...

Studies in sentiment analysis and opinion mining have been focused on several aspects of opinions, such as their automatic extraction, identification of their polarity (positive, negative or neutral), the entities or facets involved, and so on. However, to the best of our knowledge, no sentiment analysis approach has considered the automatic identi...