Medical social networks content mining for a semantic annotation

2021 ◽  
Vol 12 (1) ◽  
Author(s):  
Mouhamed Gaith Ayadi ◽  
Riadh Bouslimi ◽  
Jalel Akaichi
Keyword(s):  
Social Networks ◽  
Semantic Annotation ◽  
Content Mining

scholarly journals Personality identity in the terms of virtualization of being

2020 ◽  
Vol 210 ◽  
pp. 20016
Author(s):  
Zinaida Ryabikina ◽  
Ekaterina Bogomolova ◽  
Lyudmila Ozhigova
Keyword(s):  
Social Networks ◽  
Negative Impact ◽  
Virtual Space ◽  
The Other ◽  
Web Content ◽  
Virtual Communication ◽  
Major Activity ◽  
Communication Partner ◽  
Content Mining ◽  
And Control

The Internet users have been studied in the terms of a positive or negative impact on personality existence and co-existence in the context of virtual reality. Personality activity focus on backing up their own identity during the interaction with the Other in the virtual co-existence space. The web content mining of opinions expressed by social networks on thematic forums shows that major activity drivers in the virtual space of social networks are communicative, affiliative and self-assertive drivers. This is due to a personality's aim at backing up their identity in co-existence with the Other. The FIRO-B questionnaire has revealed relevant dominance of virtual communication participants' own activity over activity expected from their communication partner regarding the scales of inclusion and control. The opportunity to be an agent for "both" (themselves and their virtual communication partner) in the fields of inclusion and control makes it easier to gain a personality's required confirmation of their identity in relationship with the Other as well as get reassured that their being has been successfully extended into the Other's agent world (to personalize). Virtualization of a personality's relationship carries risks for identity being simulated due to non-availability of a true dialogue with the Other.

scholarly journals Privacy-driven access control in social networks by means of automatic semantic annotation

2016 ◽  
Vol 76 ◽  
pp. 12-25 ◽  
Author(s):  
Malik Imran-Daud ◽  
David Sánchez ◽  
Alexandre Viejo
Keyword(s):  
Social Networks ◽  
Access Control ◽  
Semantic Annotation

scholarly journals Semantic analysis of Twitter content

2021 ◽  
Author(s):  
Yue Feng
Keyword(s):  
Social Networks ◽  
Semantic Analysis ◽  
State Of The Art ◽  
Semantic Annotation ◽  
Semantic Relatedness ◽  
Entity Linking ◽  
Knowledge Resources ◽  
Plain Text ◽  
Analysis Techniques ◽  
Structured Knowledge

Semantic analysis is the process of shifting the understanding of text from the levels of phrases, clauses, sentences to the level of semantic meanings. Two of the most important semantic analysis tasks include 1) semantic relatedness measurement and 2) entity linking. The semantic relatedness measurement task aims to quantitatively identify the relationships between two words or concepts based on the similarity or closeness of their semantic meaning whereas the entity linking task focuses on linking plain text to structured knowledge resources, e.g. Wikipedia to provide semantic annotation of texts. A limitation of current semantic analysis approaches is that they are built upon traditional documents which are well structured in formal English, e.g. news; however, with the emergence of social networks, enormous volumes of information can be extracted from the posts on social networks, which are short, grammatically incorrect and can contain special characters or newly invented words, e.g. LOL, BRB. Therefore, traditional semantic analysis approaches may not perform well for analysing social network posts. In this thesis, we build semantic analysis techniques particularly for Twitter content. We build a semantic relatedness model to calculate semantic relatedness between any two words obtained from tweets and by using the proposed semantic relatedness model, we semantically annotate tweets by linking them to Wikipedia entries. We compare our work with state-of-the-art semantic relatedness and entity linking methods that show promising results.

A Hybrid Approach for Solving the Semantic Annotation Problem in Semantic Social Networks

2013 ◽  
Vol 65 (1) ◽  
pp. 25-33
Author(s):  
Pablo Camarillo-Ramírez ◽  
J. Carlos Conde-Ramírez ◽  
Abraham Sánchez-López
Keyword(s):  
Social Networks ◽  
Semantic Annotation ◽  
Hybrid Approach

scholarly journals SEMO: A Framework for Customer Social Networks Analysis Based on Semantics

2010 ◽  
Vol 25 (2) ◽  
pp. 178-188 ◽  
Author(s):  
Ángel García-Crespo ◽  
Ricardo Colomo-Palacios ◽  
Juan Miguel Gómez-Berbís ◽  
Belén Ruiz-Mezcua
Keyword(s):  
Social Networks ◽  
Language Processing ◽  
Semantic Annotation ◽  
Customer Relationship ◽  
Paradigm Change ◽  
Social Networks Analysis ◽  
Management Domain ◽  
Experimental Implementation ◽  
Networks Analysis ◽  
Crm 2.0

The increasing importance of the Internet in most domains has brought about a paradigm change in consumer relations. The influence of Social Networks has entered the Customer Relationship Management domain under the coined term CRM 2.0. In this context, the need to understand and classify the interactions of customers by means of new platforms has emerged as a challenge for both researchers and professionals worldwide. This is the perfect scenario for the use of SEMO, a platform for Customer Social Networks Analysis based on Semantics and emotion mining. The platform benefits from both semantic annotation and classification and text analysis, relying on techniques from the Natural Language Processing domain. The results of the evaluation of the experimental implementation of SEMO reveal a promising and viable platform from a technical perspective.

scholarly journals Semantic analysis of Twitter content

2021 ◽  
Author(s):  
Yue Feng
Keyword(s):  
Social Networks ◽  
Semantic Analysis ◽  
State Of The Art ◽  
Semantic Annotation ◽  
Semantic Relatedness ◽  
Entity Linking ◽  
Knowledge Resources ◽  
Plain Text ◽  
Analysis Techniques ◽  
Structured Knowledge

Semantic analysis is the process of shifting the understanding of text from the levels of phrases, clauses, sentences to the level of semantic meanings. Two of the most important semantic analysis tasks include 1) semantic relatedness measurement and 2) entity linking. The semantic relatedness measurement task aims to quantitatively identify the relationships between two words or concepts based on the similarity or closeness of their semantic meaning whereas the entity linking task focuses on linking plain text to structured knowledge resources, e.g. Wikipedia to provide semantic annotation of texts. A limitation of current semantic analysis approaches is that they are built upon traditional documents which are well structured in formal English, e.g. news; however, with the emergence of social networks, enormous volumes of information can be extracted from the posts on social networks, which are short, grammatically incorrect and can contain special characters or newly invented words, e.g. LOL, BRB. Therefore, traditional semantic analysis approaches may not perform well for analysing social network posts. In this thesis, we build semantic analysis techniques particularly for Twitter content. We build a semantic relatedness model to calculate semantic relatedness between any two words obtained from tweets and by using the proposed semantic relatedness model, we semantically annotate tweets by linking them to Wikipedia entries. We compare our work with state-of-the-art semantic relatedness and entity linking methods that show promising results.

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