Quality & Quantity

, Volume 52, Issue 3, pp 1173–1192 | Cite as

The use of network analysis to handle semantic differential data

  • Giuseppe Giordano
  • Ilaria Primerano


The aim of this paper is to propose a method to transform semantic differential data into a network whose graph representation is interpreted as an empirical network of adjectives. The graph is constituted by the adjectives of the semantic differential task. Two adjectives are linked depending on the scoring assigned by a set of respondents. The proposed approach aims at using concepts and methods of Social Network Analysis to explore the network structure and study roles and positions of dominant adjectives. A simulation design has been realized to assess the stability of results under different conditions, i.e. in order to set the optimal threshold in presence of different data generator processes. A case study carried out on real data shows how the emerging network of adjectives can be effectively used to define the concept arising from a semantic differential task.


Connected component Network of adjectives Semantic space Weighted network 


  1. Borgatti, S.P., Everett, M.G.: Models of core/periphery structures. Soc. Netw. 21(4), 375–395 (2000)CrossRefGoogle Scholar
  2. Box, G.E.P., Hunter, J.S., Hunter, W.G.: Statistics for Experimenters: Design, Innovation, and Discovery, 2nd edn. Wiley, Hoboken (2005)Google Scholar
  3. Butts, C.T.: sna: Tools for Social Network Analysis. R package version 2.3-1 (2013).
  4. Carrington, P.J., Scott, J., Wasserman, S. (eds.): Models and Methods in Social Network Analysis. Cambridge University Press, New York (2005)Google Scholar
  5. Csardi, G., Nepusz, T.: The igraph software package for complex network research, Int. J. Complex Syst. 1695(5), 1–9 (2006).
  6. Doreian, P., Batagelj, V., Ferligoj, A.: Generalized Blockmodeling. Cambridge University Press, New York (2005)Google Scholar
  7. Freeman, L.: A set of measures of centrality based upon betweenness. Sociometry, 35–41 (1977)Google Scholar
  8. Freeman, L.: The Development of Social Network Analysis. A Study in the Sociology of Science. Empirical Press, Vancouver (2004)Google Scholar
  9. R Core Team: R: A language and environment for statistical computing. R Foundation for Statistical Computing, Vienna, Austria (2016).
  10. Hanneman, R.A., Riddle, M.: Introduction to Social Network Methods. University of California, Riverside (2005)Google Scholar
  11. Heise, D.R.: Some methodological issues in semantic differential research. Psychol. Bull. 72(6), 406–422 (1969)CrossRefGoogle Scholar
  12. Heise, D.R.: The semantic differential and attitude research. In: Summers, G.F. (ed.) Attitude Measurement, pp. 235–253. Rand McNally, Chicago (1970)Google Scholar
  13. Jensen, P., Morini, M., Karsai, M., Venturini, T., Vespignani, A., Jacomy, M., Cointet, J.P., Mercklè, P., Fleury, E.: Detecting global bridges in networks. J. Complex Netw. 4(3), 319–329 (2016)CrossRefGoogle Scholar
  14. Lorrain, F., White, H.C.: Structural equivalence of individuals in social networks. J. Math. Sociol. 1(1), 49–80 (1971)CrossRefGoogle Scholar
  15. Newman, M.E.J.: Detecting community structure in networks. Eur. Phys. J. B Conden. Matter Complex Syst. 38(2), 321–330 (2004)CrossRefGoogle Scholar
  16. Osgood, C.E., Suci, G., Tannenbaum, P.: The Measurement of Meaning. University of Illinois Press, Urbana (1957)Google Scholar
  17. Wasserman, S., Faust, K.: Social Network Analysis: Methods and Applications. Cambridge University Press, New York (1994)CrossRefGoogle Scholar
  18. White, D.R., Reitz, K.P.: Graph and semigroup homomorphisms on networks of relations. Soc. Netw. 5(2), 193–234 (1983)CrossRefGoogle Scholar

Copyright information

© Springer Science+Business Media Dordrecht 2017

Authors and Affiliations

  1. 1.Department of Economics and StatisticsUniversity of SalernoFiscianoItaly

Personalised recommendations