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 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.

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.

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