scholarly journals Analyzing the Effect of Negation in Sentiment Polarity of Facebook Dialectal Arabic Text

2021 ◽  
Vol 11 (11) ◽  
pp. 4768
Author(s):  
Sanaa Kaddoura ◽  
Maher Itani ◽  
Chris Roast
Keyword(s):  
Social Networks ◽  
Social Network ◽  
Sentiment Analysis ◽  
Arabic Language ◽  
Network Data ◽  
Arabic Text ◽  
Social Network Data ◽  
Dialectal Arabic ◽  
The Impact ◽  
Modern Standard

With the increase in the number of users on social networks, sentiment analysis has been gaining attention. Sentiment analysis establishes the aggregation of these opinions to inform researchers about attitudes towards products or topics. Social network data commonly contain authors’ opinions about specific subjects, such as people’s opinions towards steps taken to manage the COVID-19 pandemic. Usually, people use dialectal language in their posts on social networks. Dialectal language has obstacles that make opinion analysis a challenging process compared to working with standard language. For the Arabic language, Modern Standard Arabic tools (MSA) cannot be employed with social network data that contain dialectal language. Another challenge of the dialectal Arabic language is the polarity of opinionated words affected by inverters, such as negation, that tend to change the word’s polarity from positive to negative and vice versa. This work analyzes the effect of inverters on sentiment analysis of social network dialectal Arabic posts. It discusses the different reasons that hinder the trivial resolution of inverters. An experiment is conducted on a corpus of data collected from Facebook. However, the same work can be applied to other social network posts. The results show the impact that resolution of negation may have on the classification accuracy. The results show that the F1 score increases by 20% if negation is treated in the text.

scholarly journals Questions and features of automated social networks monitoring with support of intelligent user messages analysis.

2014 ◽  
Vol 2014 (4) ◽  
pp. 146-152 ◽  
Author(s):  
Александр Подвесовский ◽  
Aleksandr Podvesovskiy ◽  
Дмитрий Будыльский ◽  
Dmitriy Budylskiy
Keyword(s):  
Social Networks ◽  
Social Network ◽  
Text Mining ◽  
Sentiment Analysis ◽  
Opinion Mining ◽  
Software Implementation ◽  
Network Data ◽  
Research Directions ◽  
Social Network Data ◽  
Monitoring Model

An opinion mining monitoring model for social networks introduced. The model includes text mining processing over social network data and uses sentiment analysis approach in particular. Practical usage results of software implementation and its requirements described as well as further research directions.

scholarly journals Effectiveness of the Execution and Prevention of Metric-Based Adversarial Attacks on Social Network Data †

Information ◽  
2020 ◽  
Vol 11 (6) ◽  
pp. 306
Author(s):  
Nikolaus Nova Parulian ◽  
Tiffany Lu ◽  
Shubhanshu Mishra ◽  
Mihai Avram ◽  
Jana Diesner
Keyword(s):  
Social Networks ◽  
Social Network ◽  
Information Diffusion ◽  
Social Systems ◽  
Target Function ◽  
Centrality Measures ◽  
Network Data ◽  
Social Network Data ◽  
Empirical Networks ◽  
The Impact

Observed social networks are often considered as proxies for underlying social networks. The analysis of observed networks oftentimes involves the identification of influential nodes via various centrality measures. This paper brings insights from research on adversarial attacks on machine learning systems to the domain of social networks by studying strategies by which an adversary can minimally perturb the observed network structure to achieve their target function of modifying the ranking of a target node according to centrality measures. This can represent the attempt of an adversary to boost or demote the degree to which others perceive individual nodes as influential or powerful. We study the impact of adversarial attacks on targets and victims, and identify metric-based security strategies to mitigate such attacks. We conduct a series of controlled experiments on synthetic network data to identify attacks that allow the adversary to achieve their objective with a single move. We then replicate the experiments with empirical network data. We run our experiments on common network topologies and use common centrality measures. We identify a small set of moves that result in the adversary achieving their objective. This set is smaller for decreasing centrality measures than for increasing them. For both synthetic and empirical networks, we observe that larger networks are less prone to adversarial attacks than smaller ones. Adversarial moves have a higher impact on cellular and small-world networks, while random and scale-free networks are harder to perturb. Also, empirical networks are harder to attack than synthetic networks. Using correlation analysis on our experimental results, we identify how combining measures with low correlation can aid in reducing the effectiveness of adversarial moves. Our results also advance the knowledge about the robustness of centrality measures to network perturbations. The notion of changing social network data to yield adversarial outcomes has practical implications, e.g., for information diffusion on social media, influence and power dynamics in social systems, and developing solutions to improving network security.

Network Basics: Points, Lines, and Positions

2020 ◽  
pp. 15-33
Author(s):  
Ryan Light ◽  
James Moody
Keyword(s):  
Social Networks ◽  
Social Network ◽  
Network Analysis ◽  
Ethical Issues ◽  
Building Blocks ◽  
Levels Of Analysis ◽  
Network Data ◽  
Social Network Data ◽  
Basic Concepts ◽  
Statistical Approaches

This chapter presents an introduction to the basic concepts central to social network analysis. Written for those with little experience in the approach, the chapter aims to provide the necessary tools to dig deeper into exploring social networks via the subsequent chapters in this volume. It begins by introducing the building blocks of networks—nodes and edges—and their characteristics. Next, it outlines several of the major dimensions of network analysis, including the implications of boundary specification and levels of analysis. It also briefly introduces statistical approaches to networks and network data collection. The chapter concludes with a discussion of ethical issues that arise when collecting and analyzing social network data.

scholarly journals Edge overlap in weighted and directed social networks

2021 ◽  
pp. 1-15
Author(s):  
Heather Mattie ◽  
Jukka-Pekka Onnela
Keyword(s):  
Social Networks ◽  
Social Network ◽  
Social Interactions ◽  
Network Data ◽  
Directed Networks ◽  
Rural Villages ◽  
Social Network Data ◽  
Structure Strength ◽  
Mean And Variance ◽  
The Mean

Abstract With the increasing availability of behavioral data from diverse digital sources, such as social media sites and cell phones, it is now possible to obtain detailed information about the structure, strength, and directionality of social interactions in varied settings. While most metrics of network structure have traditionally been defined for unweighted and undirected networks only, the richness of current network data calls for extending these metrics to weighted and directed networks. One fundamental metric in social networks is edge overlap, the proportion of friends shared by two connected individuals. Here, we extend definitions of edge overlap to weighted and directed networks and present closed-form expressions for the mean and variance of each version for the Erdős–Rényi random graph and its weighted and directed counterparts. We apply these results to social network data collected in rural villages in southern Karnataka, India. We use our analytical results to quantify the extent to which the average overlap of the empirical social network deviates from that of corresponding random graphs and compare the values of overlap across networks. Our novel definitions allow the calculation of edge overlap for more complex networks, and our derivations provide a statistically rigorous way for comparing edge overlap across networks.

Mining of Leaders in Mobile Telecom Social Networks

2016 ◽  
pp. 68-77
Author(s):  
Mantian (Mandy) Hu
Keyword(s):  
Social Networks ◽  
Big Data ◽  
Social Network ◽  
Information Diffusion ◽  
Network Data ◽  
Massive Datasets ◽  
Social Network Data ◽  
The Social ◽  
Marketing Efficiency ◽  
Mobile Telecom

In the age of Big Data, the social network data collected by telecom operators are growing exponentially. How to exploit these data and mine value from them is an important issue. In this article, an accurate marketing strategy based on social network is proposed. The strategy intends to help telecom operators to improve their marketing efficiency. This method is based on mutual peers' influence in social network, by identifying the influential users (leaders). These users can promote the information diffusion prominently. A precise marketing is realized by taking advantage of the user's influence. Data were collected from China Mobile and analyzed. For the massive datasets, the Apache Spark was chosen for its good scalability, effectiveness and efficiency. The result shows a great increase of the telecom traffic, compared with the result without leader identification.

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