When social computing meets soft computing: opportunities and insights

Abstract

The characteristics of the massive social media data, diverse mobile sensing devices as well as the highly complex and dynamic user’s social behavioral patterns have led to the generation of huge amounts of high dimension, uncertain, imprecision and noisy data from social networks. Thanks to the emerging soft computing techniques which unlike the conventional hard computing. It is widely used for coping with the tolerant of imprecision, uncertainty, partial truth, and approximation. One of the most important and promising applications is social network analysis (SNA) that is the process of investigating social structures and relevant properties through the use of network and graph theories. This paper aims to survey various SNA approaches using soft computing techniques such as fuzzy logic, formal concept analysis, rough sets theory and soft set theory. In addition, the relevant software packages about SNA are clearly summarized.

Sentiment Analysis on Social Network

“…A talented heading aimed at upcoming effort remains toward explore the presentation of highlight assertion approaches happening various AI classifiers also toward assess the perfect aimed at irritated space hypothesis evaluation by other zone than film examines. [9] The creators discussed when social figuring meets sensitive handling. Fragile figuring frameworks like feathery sets, neural frameworks, inherited estimations, brutal sets, sensitive sets and their hybridizations, have starting late been commonly used to deal with data mining issues.…”

The Efficiency of a DoParallel Algorithm and an FCA Network Graph Applied to Recommendation System

“…According to this reason, many researchers are tried to develop various new methods such as how to classify users into several communities by using their personal information or create a technique that can pick up the cluster of the users by using their connection [17][18][19][20][21]. Some work that concerning this research were presented by Hao et al [26], his work shown the opportunities and insights when social computing meets soft computing, and Ryu et al [27] predicted the unemployment rate using social media analysis. One of those methodologies is the k-clique, it is typically used to analyze data of the users in the vast social network and classify them into various groups that depend on their personal information or their connections [25][26][27][28][29].…”

“…Some work that concerning this research were presented by Hao et al [26], his work shown the opportunities and insights when social computing meets soft computing, and Ryu et al [27] predicted the unemployment rate using social media analysis. One of those methodologies is the k-clique, it is typically used to analyze data of the users in the vast social network and classify them into various groups that depend on their personal information or their connections [25][26][27][28][29]. In the past few years, some researchers used the k-clique method as an algorithm model in the recommendation system [2].…”

On the Computation of Concept Stability Based on Maximal Non-Generator for Social Networking Services

“…Then by the concept generation algorithm, the network structure is transformed into concept form which is extracted knowledge by FCA. The detailed process of how to use FCA in social networks analysis can refer to [2,4]. In practice, the overwhelming size of the extracted knowledge, induced by formal concepts, limits its extensive use.…”