Semantic Web-Based Social Media Analysis

Semantic Search in Social Media Analysis

“…The information stored in ontologies can easily be retrieved using a specialized query language, known as SPARQL and new relationships inside the data can be discovered through inference using semantic reasoning engines. Ontologies have already been successfully used in many social media analysis tasks, including detecting trending news and topics [13], modelling of extreme financial events [14], understanding people behaviour in an earthquake evacuation scenario [15], extracting user preferences regarding the characteristics of a product [4] and analysing the emotions expressed in social media messages [16]. The concepts required in order to semantically search information in previously collected social media messages can be grouped in the following three categories:  concepts that describe the social media specific knowledge;  concepts that represent the analysed entities, such as products or service;  concepts that provide a connection between the social media messages, the analysed entities, as well as with any other additional data obtained using NLP techniques, such as sentiment or emotion analysis.…”

“…For representing the social media concepts and their properties, we have chosen to use the ontology that we have proposed in [16], which extends well recognized ontologies such as SIOC and FOAF with the concepts specific to Twitter and follows the recommended ontology modelling best practices. The tw prefix is used in the following to denote classes or properties belonging to this ontology.…”

“…While many sentiment analysis algorithms have been proposed in the scientific literature, we have chosen to use the bag of words model described in [16], given its low complexity and adequate results.…”

Grey social media engagement analysis

“…Making a short analysis, it can be seen that only in 2015, there were recorded a number of 64672 papers written in the social media area, 2687 focusing mainly on the online social networks. (Delcea et al, 2014a) The main focus areas were: social issues such as: confidentiality and privacy (Lewis et al, 2008;Nosko et al, 2010), age gap (Pfeil et al, 2009), social activity (Cheung et al, 2011;Cheung and Lee, 2010), sentiment analysis (Cotfas et al, 2015(Cotfas et al, , 2016, social assets (Ellison et al, 2007), communication (Subrahmanyam et al, 2008), addiction (Benevenuto et al, 2009); elearning (Cotfas, 2011;Cotfas and Roxin, 2013;Greenhow and Robelia, 2009); risk within companies (Schniederjans et al, 2013); e-commerce and online public goods (Wasko et al, 2009); health issues (Guseh et al, 2009;Thompson et al, 2008)and patient-doctor relationship in social media (Bosslet et al, 2011). As for the main research areas of the papers published on Web-of-Science (WoS, 2016) see Considering just the online social network research, in Fig.…”

Grey clustering in online social networks

“…The main social issues that were addressed by the scientific world were aspects regarding confidentiality and privacy [8,9], age gap [10], sentiment analysis [11,12], social activity [13,14], addiction [15] and social assets [16]. The impact of word-ofmouth communication was studied by Brown [17] and the viral marketing by Subramani and Rajagopalan [18].…”