The Semantic Network Approach: Opportunities and Restrictions (Example of Inflation Image in the Media)

Abstract

This article focuses on there being a need for tools that can facilitate coding and analysis processes for news reports. The study was based on a set of economic news replete with specific terms, interpretations, expertise and metaphorical description of events. In most cases it can be argued that the content of the texts is complicated, thus “classical” content analysis may require additional iterations and increased attention to the analytical procedure. The study highlights the methodological, analytical features of the semantic network approach (SNA) in comparison with the content analysis and Text Mining approaches based on analyzing six economic news items containing the terms “rising prices” and “inflation”. SNA is distinguished by simplification of large unstructured data processing with emphasis on content. The preparation and calculation of network metrics for each news report leads to the most significant concepts being reflected. That simplifies the content analysis of a larger body of texts. In several cases visualization shows different semantic positions of “inflation” being a synonym for “rising prices” depending on the topic. As an important result, regardless of the volume and visual structure of the news message, these terms can be considered as leading in the corresponding storylines that can help conduct a discourse analysis with their mention. It is assumed that this approach will become a “supporting” tool for further quantitative and qualitative analysis of news reports, particularly on economic topics. The technical features of text preparation and semantic modeling programs can be considered as potential limitations of the approach, especially in the space of Text Mining.

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