Semantic network analysis (SemNA): A tutorial on preprocessing, estimating, and analyzing semantic networks.

Analysis of political communication is an important aspect of political research. Thematic content analysis has yielded considerable success both with manual and automatic coding, but Semantic Network Analysis has proven more difficult, both for humans and for the computer. This article presents a system for an automated Semantic Network Analysis of Dutch texts. The system automatically extracts relations between political actors based on the output of syntactic analysis of Dutch newspaper articles. Specifically, the system uses pattern matching to find source constructions and determine the semantic agent and patient of relations, and name matching and anaphora resolution to identify political actors. The performance of the system is judged by comparing the extracted relations to manual codings of the same material. Results on the level of measurement indicate acceptable performance. We also estimate performance at the levels of analysis by using a case study of media authority, resulting in good correlations between the theoretical variables derived from the automatic and manual analysis. Finally, we test a number of substantive hypotheses with regression models using the automatic and manual output, resulting in highly similar models in each case. This suggests that our method has sufficient performance to be used to answer relevant political questions in a valid way.

Download Full-text

Supplemental Material for Semantic Network Analysis (SemNA): A Tutorial on Preprocessing, Estimating, and Analyzing Semantic Networks

Psychological Methods ◽

10.1037/met0000463.supp ◽

2021 ◽

Keyword(s):

Network Analysis ◽

Semantic Network ◽

Semantic Networks ◽

Semantic Network Analysis

Download Full-text

Semantic Network Analysis (SemNA): A Tutorial on Preprocessing, Estimating, and Analyzing Semantic Networks

10.31234/osf.io/eht87 ◽

2019 ◽

Cited By ~ 2

Author(s):

Alexander P. Christensen ◽

Yoed Kenett

Keyword(s):

Network Analysis ◽

Cognitive Processes ◽

Semantic Network ◽

Semantic Networks ◽

Semantic Network Analysis ◽

Real World Data ◽

Analysis Pipeline ◽

Psychological Phenomena ◽

Semantic Data ◽

R Packages

To date, the application of semantic network methodologies to study cognitive processes in psychological phenomena has been limited in scope. One barrier to broader application is the lack of resources for researchers unfamiliar with the approach. Another barrier, for both the unfamiliar and knowledgeable researcher, is the tedious and laborious preprocessing of semantic data. In this article, we aim to minimize these barriers by offering a comprehensive semantic network analysis pipeline (preprocessing, estimating, and analyzing networks), and an associated R tutorial that uses a suite of R packages to accommodate this pipeline. Two of these packages, SemNetDictionaries and SemNetCleaner, promote an efficient, reproducible, and transparent approach to preprocessing verbal fluency data. The third package, SemNeT, provides methods and measures for analyzing and statistically comparing semantic networks via a point-and-click graphical user interface. Using real-world data, we present a start-to-finish pipeline from raw data to semantic network analysis results. This article aims to provide resources for researchers, both the unfamiliar and knowledgeable, that reduce some of the barriers for conducting semantic network analysis.

Download Full-text

Emergent Meaning Structures: A Socio-Semantic Network Analysis of Artistic Collectives

10.31235/osf.io/42k3c ◽

2018 ◽

Author(s):

Nikita Basov ◽

Wouter De Nooy ◽

Aleksandra Nenko

Keyword(s):

Network Analysis ◽

Social Structure ◽

Small Groups ◽

Social Practice ◽

Semantic Network ◽

Semantic Networks ◽

Artistic Practice ◽

Semantic Network Analysis ◽

Meaning Structure ◽

Specific Meaning

This paper explores meaning structures in the social practice of small groups. While social and institutional fields impose meaning structures, they are put to practice (emerge) in the context of specific activities that take place within a field. Collaborating in small groups, field participants form such practical contexts. It enables playing on gaps and overlaps among imposed meaning structures and joint creation of emergent meaning structures that define them as a social group. Difficult to capture, emergent meaning structures are largely disregarded by institutional and field perspectives on meaning structures. As a consequence, the importance of collective practice to meaning structures is underrated.We investigate imposed and emergent meaning structures in artistic collectives. The field of contemporary art does not impose its meaning structure explicitly, so meaning structures that emerge in artistic practice are relatively free to vary across social groups. In particular, we study two St. Petersburg collectives of artists, who intensely interact with each other and engage in joint creative work and exhibitions. We show that these collectives elaborate their own meaning structures within the framework of field-specific meaning structures, blending meanings corresponding to the different fields and field positions occupied by members of the collective.The duality of semantic and social structure is central to the notion of meaning structures. We use word collocations in natural language as semantic structure and interaction ties as social structure in a mixed methods socio-semantic network analysis. In this approach, social networks help to understand semantic networks.

Download Full-text