Violence Detection over Online Social Networks: An Arabic Sentiment Analysis Approach

Abstract

<p class="0abstract">Today, the influence of the social media on different aspects of our lives is increasing, many scholars from various disciplines and majors looking at the social media networks as the ongoing revolution. In Social media networks, many bonds and connections can be established whether being direct or indirect ties. In fact, Social networks are used not only by people but also by companies. People usually create their own profiles and join communities to discuss different common issues that they have interest in. On the other hand, companies also can create their virtual presence on the social media networks to benefit from this media to understand the customers and gather richer information about them. With all of the benefits and advantages of social media networks, they should not always be seen as a safe place for communicating, sharing information and ideas, and establishing virtual communities. These information and ideas could carry with them hatred speeches that must be detected to avoid raising violence. Therefore, web content mining can be used to handle this issue. Web content mining is gaining more concern because of its importance for many businesses and institutions. Sentiment Analysis (SA) is an important sub-area of web content mining. The purpose of SA is to determine the overall sentiment attitude of writer towards a specific entity and classify these opinions automatically. There are two main approaches to build systems of sentiment analysis: the machine learning approach and the lexicon-based approach. This research presents the design and implementation for violence detection over social media using machine learning approach. Our system works on Jordanian Arabic dialect instead of Modern Standard Arabic (MSA). The data was collected from two popular social media websites (Facebook, Twitter) and has used native speakers to annotate the data. Moreover, different preprocessing techniques have been used to show their effect on our model accuracy. The Arabic lexicon was used for generating feature vectors and separate them to features set. Here, we have three well known machine learning algorithms: Support Vector Machine (SVM), Naive Bayes (NB) and k-Nearest Neighbors (KNN). Building on this view, Information Science Research Institute’s (ISRI) stemming and stop word file as a result of preprocessing were used to extract the features. Indeed, several features have been extracted; however, using the SVM classifier reveals that unigram and features extracted from lexicon are characterized by the highest accuracy to detect violence.</p>

Deep Learning Technique of Sentiment Analysis for Twitter Database

“…It mentioned in the direction of the intention of NL Processing, manuscript examination, calculation linguistics as well as biometric clarification to analytically recognize, remove, count and revise various states and individual information. Most of the times the sentiment investigation goes away from the classification of manuscripts to discover estimations and classifies them as undesirable or desirable, negative or positive [12][13][14].…”

Proceedings of the First Workshop on Bangla Language Processing (BLP-2023)

“…Traditional machine learning (ML) techniques have been applied for violence text detection in online social media platforms (Khalafat et al, 2021). Machine learning algorithms such as Support Vector Machine (SVM), Naive Bayes (NB), and K-Nearest Neighbours (KNN) have been utilized where SVM provides the best result for violenceinciting text detection.…”

“…From chatbots and machine translation to information retrieval and sentiment analysis, NLP has become an indispensable tool for extracting meaning from the vast sea of human-generated text (Sun et al, 2022). Among the diverse array of NLP tasks, sentiment analysis, and violence incitement detection stand out as pivotal areas with far-reaching implications for societal well-being and communication (Khalafat et al, 2021;Castorena et al, 2021).…”

Improved Ant Lion Optimizer with Deep Learning Driven Arabic Hate Speech Detection

“…It utilized a convolutional neural network (CNN) and character-level representation. Khalafat et al [18] introduce the design and application for violence detection on mass media utilizing ML techniques. This system operates in the Jordanian Arabic dialect rather than Modern Standard Arabic (MSA).…”