Social media platforms and some well-known websites such as Google and Amazon customise the webpages they display to their users, based on their preferences and browsing habits. Therefore, the information in the browser window is personalised and it is based on a so-called “user profile”.
With the introduction of the Semantic Web concept, researchers have looked at the users’ profiles from a semantic angle.
Gentile et al. (2011) carried out research to profile users from a semantic angle and based on informal communication exchange through email. They claim their study proposes a solution to modelling user expertise from regular email exchange in organisations, where very often, lost knowledge could be easily rescued and stored for re-use purposes to enable quick transfer to newcomers for increasing efficiency in workplace:
“Extracting information from informal communication exchanges could be hugely beneficial for knowledge management inside an organisation, as it offers means to recover buried knowledge without any additional effort from individuals and respecting their natural communication patterns” (p. 13).
“Given the variety and recency of topics people discuss on Twitter, semantic user profiles generated from Twitter posts moreover promise to be beneficial for other applications on the Social Web as well” (Fabian et al., 2011, p.375).
Fabian et al. introduce a method to analyse tweets and the information linked to tweets via URLs. They created semantic user profiles; they then enriched with relevant elements featured in the webpages that are highlighted in the tweets through their URLs. They found out that Twitter user profiles, where URLs are employed in the tweet bodies, could fit in three types of models, based on: hashtag, topic and entity.
To model the profiles, the researchers applied research strategies to discover tweet-news relations, URL categories, content analysis (by employing the Bag of Words method), hashtag analysis, a strategy to identify entities in the text by employing OpenCalais to extract entities and topics from both tweet corpus and the linked webpages. They conclude that their study reveals that analysing tweet bodies could be a way of semantically profiling a Twitter user.
Semantic network analysis
Drieger (2013) proposes to employ a semantic network model to represent “visual text analytics to support knowledge building” (p.4): “Semantic networks allow to model semantic relationships (Sowa, 1991) that are represented in a graph with labelled nodes and edges” (p.4).
Figure 1: A semantic network part of EURes Twitter communications in 2012 (The “website” node is not connected to the network as it did not qualify for three minimum links with the other nodes)
The lexical dimension of a semantic network consists of nodes (the words), links (the connection between the words) and labels (the word denomination). Doerfel and Barnett (1999) provided a complete definition of the semantic network analysis when CAQDAS was in its earlier years:
Semantic network analysis, similar to network analysis, is both a research method and a theoretical framework. Semantic network analysis differs from traditional network methods because it focuses on the structure of a system based on shared meaning rather than on links among communication partners. In other words, two nodes are connected in a semantic network to the extent that their uses of concepts overlap (p.589).
Furthermore semantic network analysis implies:
…a theoretical foundation based on cognitive processes. Learning theorists argue that words are hierarchically clustered in memory. Thus, spatial models that illustrate the relationships among words are representative of meaning. As a result, studies have turned to analysis of text with network analysis techniques (Danoswki, 1982; Jang & Barnett, 1995; Rice & Danowski, 1993; Stohl, 1993)” (p.590).
Doerfel and Barnett (1999) acknowledge the use of the WORDij software suite, and in particular of its component, WORDLINK, which has been in place since 1993 to assist with the semantic network analysis:
“Semantic network analysis requires a content analysis of textual data to determine the most frequently used symbols. The analysis then provides the relationship among these symbols and how they co-vary with the members of the social system. Although this process traditionally has been conducted by hand, computer-based analysis software has been developed and used to describe the semantic structure of textual data. For example, see WORDLINK (Danowski, 1993)” (p.591).
The optimal visual representation of a semantic network would be a spring embedded graph, which consists of nodes and arrows. The nodes are connected by arrows “in a two dimensional plane with some separation, while attempting to keep connected nodes reasonably close together. Each node in the graph is modelled as a charged particle, thereby causing a repulsive force between every pair of nodes. Each edge is modelled as a spring that exerts an attractive force between the pair of nodes it connects” (Mutton and Golbeck, 2003, p.300).
 Gentile, A.L. et al. (2011), Extracting Semantic User Networks From Informal Communication Exchange, in The 10th International Semantic Web Conference, Bonn, Germany
 Fabian, A. et al. (2011), “Semantic Enrichment of Twitter Posts for User Profile Construction on the Social Web” in The Semantic Web: Research and Applications, Lecture Notes in Computer Science Volume 6644, pp 375-389
 Drieger, Ph. (2013), “Semantic Network Analysis as a Method for Visual Text Analytics” in Procedia – Social and Behavioral Sciences, Vol. 79, pp. 4–17, 9th Conference on Applications of Social Network Analysis (ASNA)
 Doerfel, M.L. and Barnett, (1999), “A Semantic Network Analysis of the International Communication Association” in Human Communication Research, Vol. 25/4, pp. 589-603
 CAQDAS stands for Computer Assisted Qualitative Data Analysis Software