15,445 research outputs found
Topic Similarity Networks: Visual Analytics for Large Document Sets
We investigate ways in which to improve the interpretability of LDA topic
models by better analyzing and visualizing their outputs. We focus on examining
what we refer to as topic similarity networks: graphs in which nodes represent
latent topics in text collections and links represent similarity among topics.
We describe efficient and effective approaches to both building and labeling
such networks. Visualizations of topic models based on these networks are shown
to be a powerful means of exploring, characterizing, and summarizing large
collections of unstructured text documents. They help to "tease out"
non-obvious connections among different sets of documents and provide insights
into how topics form larger themes. We demonstrate the efficacy and
practicality of these approaches through two case studies: 1) NSF grants for
basic research spanning a 14 year period and 2) the entire English portion of
Wikipedia.Comment: 9 pages; 2014 IEEE International Conference on Big Data (IEEE BigData
2014
Anisotropic Radial Layout for Visualizing Centrality and Structure in Graphs
This paper presents a novel method for layout of undirected graphs, where
nodes (vertices) are constrained to lie on a set of nested, simple, closed
curves. Such a layout is useful to simultaneously display the structural
centrality and vertex distance information for graphs in many domains,
including social networks. Closed curves are a more general constraint than the
previously proposed circles, and afford our method more flexibility to preserve
vertex relationships compared to existing radial layout methods. The proposed
approach modifies the multidimensional scaling (MDS) stress to include the
estimation of a vertex depth or centrality field as well as a term that
penalizes discord between structural centrality of vertices and their alignment
with this carefully estimated field. We also propose a visualization strategy
for the proposed layout and demonstrate its effectiveness using three social
network datasets.Comment: Appears in the Proceedings of the 25th International Symposium on
Graph Drawing and Network Visualization (GD 2017
Natural Language Processing for Information Retrieval and Knowledge Discovery
Natural Language Processing (NLP) is a powerful technology for the vital tasks of information retrieval (IR) and knowledge discovery (KD) which, in turn, feed the visualization systems of the present and future and enable knowledge workers to focus more of their time on the vital tasks of analysis and prediction.published or submitted for publicatio
Discourse network analysis: policy debates as dynamic networks
Political discourse is the verbal interaction between political actors. Political actors make normative claims about policies conditional on each other. This renders discourse a dynamic network phenomenon. Accordingly, the structure and dynamics of policy debates can be analyzed with a combination of content analysis and dynamic network analysis. After annotating statements of actors in text sources, networks can be created from these structured data, such as congruence or conflict networks at the actor or concept level, affiliation networks of actors and concept stances, and longitudinal versions of these networks. The resulting network data reveal important properties of a debate, such as the structure of advocacy coalitions or discourse coalitions, polarization and consensus formation, and underlying endogenous processes like popularity, reciprocity, or social balance. The added value of discourse network analysis over survey-based policy network research is that policy processes can be analyzed from a longitudinal perspective. Inferential techniques for understanding the micro-level processes governing political discourse are being developed
Data Innovation for International Development: An overview of natural language processing for qualitative data analysis
Availability, collection and access to quantitative data, as well as its
limitations, often make qualitative data the resource upon which development
programs heavily rely. Both traditional interview data and social media
analysis can provide rich contextual information and are essential for
research, appraisal, monitoring and evaluation. These data may be difficult to
process and analyze both systematically and at scale. This, in turn, limits the
ability of timely data driven decision-making which is essential in fast
evolving complex social systems. In this paper, we discuss the potential of
using natural language processing to systematize analysis of qualitative data,
and to inform quick decision-making in the development context. We illustrate
this with interview data generated in a format of micro-narratives for the UNDP
Fragments of Impact project
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