Natural Language Processing for Building and Enhancing Graph Data and Theory

How can we use user-generated content to construct, infer or refine network data? We have been tackling this problem by leveraging communication content produced and disseminated in social networks to enhance graph data. For example, we have used domain-adjusted sentiment analysis to label graphs with valence values in order to enable triadic balance assessment. The resulting method enables fast and systematic sign detection, eliminates the need for surveys or manual link labeling, and reduces issues with leveraging user-generated (meta)-data.