Identifying the Pathways for Meaning Circulation using Text Network Analysis

In this work we propose a method and algorithm for identifying the pathways for meaning circulation within a text. This is done by visualizing normalized textual data as a graph and deriving the key metrics for the concepts and for the text as a whole using network analysis. The resulting data and graph representation are then used to detect the key concepts, which function as junctions for meaning circulation within a text, contextual clusters comprised of word communities (themes), as well as the most often used pathways for meaning circulation. We then discuss several practical applications of our method ranging from automatic recovery of hidden agendas within a text and intertextual navigation graph-interfaces, to enhancing reading and writing, quick text summarization, as well as group sentiment profiling and text diagramming. We also make a quick overview of the existing computer-assisted text analysis (and, specifically, network text analysis), and text visualization methods in order to position our research in relation to the other available approaches.

[1]  Benedikt Löwe,et al.  Comparing Formal Frameworks of Narrative Structure , 2010, AAAI Fall Symposium: Computational Models of Narrative.

[2]  John Law,et al.  Science For Social Scientists , 1984 .

[3]  Bertram C. Bruce,et al.  Interacting plans , 1978, SGAR.

[4]  V. Shklovsky,et al.  Energy of Delusion: A Book on Plot , 2007 .

[5]  Benedikt Löwe,et al.  Identifying the structure of a narrative via an agent-based logic of preferences and beliefs: Formalizations of episodes from CSI: Crime Scene Investigation™ , 2009 .

[6]  Alfred North Whitehead,et al.  Modes of Thought , 1939 .

[7]  Roel Popping,et al.  Knowledge Graphs and Network Text Analysis , 2003 .

[8]  Italo Calvino,et al.  The uses of literature : essays , 1986 .

[9]  Thomas Hofmann,et al.  Probabilistic latent semantic indexing , 1999, SIGIR '99.

[10]  Giorgio Agamben,et al.  Qu'est-ce qu'un dispositif? , 2007 .

[11]  David M. Blei,et al.  Hierarchical relational models for document networks , 2009, 0909.4331.

[12]  Michael I. Jordan,et al.  Probabilistic models of text and images , 2004 .

[13]  Robert Krovetz,et al.  Viewing morphology as an inference process , 1993, Artif. Intell..

[14]  U. Brandes.  A faster algorithm for betweenness centrality , 2001 .

[15]  M. Wertheimer,et al.  Gestalt Theory , 2019, Theories and Applications of Counseling and Psychotherapy: Relevance Across Cultures and Settings.

[16]  Caryl Emerson,et al.  The Dialogic Imagination. Four Essays by M. M. Bakhtin , 1982 .

[17]  Yixin Chen,et al.  A comparison of a graph database and a relational database: a data provenance perspective , 2010, ACM SE '10.

[18]  Loet Leydesdorff,et al.  ‘Meaning’ as a sociological concept: A review of the modeling, mapping and simulation of the communication of knowledge and meaning , 2010, ArXiv.

[19]  Peter W. Foltz,et al.  An introduction to latent semantic analysis , 1998 .

[20]  Alfred North Whitehead,et al.  Modes of Thought , 1939 .

[21]  Wendy G. Lehnert.  Plot Units and Narrative Summarization , 1981 .

[22]  Martin F. Porter,et al.  An algorithm for suffix stripping , 1997, Program.

[23]  James J. Thomas,et al.  Visualizing the non-visual: spatial analysis and interaction with information from text documents , 1995, Proceedings of Visualization 1995 Conference.

[24]  Caryl Emerson,et al.  The Dialogic Imagination: Four Essays by M. M. Bakhtin. , 1983 .

[25]  G. Zipf,et al.  The Psycho-Biology of Language , 1936 .

[26]  Andreas Noack,et al.  Energy Models for Graph Clustering , 2007, J. Graph Algorithms Appl..

[27]  Wendy G. Lehnert,et al.  Plot Units and Narrative Summarization , 1981, Cogn. Sci..

[28]  Kathleen M. Carley.  Coding Choices for Textual Analysis: A Comparison of Content Analysis and Map Analysis , 1993 .

[29]  Marie-Laure Ryan.  Diagramming narrative , 2007 .

[30]  Mark Newman,et al.  Networks: An Introduction , 2010 .

[31]  Wei Li,et al.  Pachinko allocation: DAG-structured mixture models of topic correlations , 2006, ICML.

[32]  Jean-Loup Guillaume,et al.  Fast unfolding of communities in large networks , 2008, 0803.0476.

[33]  Eyal Segal.  Energy of Delusion: A Book on Plot , 2011 .

[34]  Michael G. Dyer,et al.  The Role of Affect in Narratives , 1983, Cogn. Sci..

[35]  W. H. van Atteveldt,et al.  Semantic Network Analysis: Techniques for Extracting, Representing, and Querying Media Content , 2008 .

[36]  Roel Popping,et al.  Computer-assisted text analysis , 2000 .

[37]  U. Brandes,et al.  GraphML Progress Report ? Structural Layer Proposal , 2001 .

[38]  Leonard M. Freeman,et al.  A set of measures of centrality based upon betweenness , 1977 .

[39]  Mathieu Bastian,et al.  Gephi: An Open Source Software for Exploring and Manipulating Networks , 2009, ICWSM.

[40]  Michael I. Jordan,et al.  Latent Dirichlet Allocation , 2001, J. Mach. Learn. Res..

[41]  R. S. Woolhouse,et al.  Revolutions and Reconstructions in the Philosophy of Science. , 1981 .

[42]  Santo Fortunato,et al.  Community detection in graphs , 2009, ArXiv.

[43]  Owen Kaser,et al.  Tag-Cloud Drawing: Algorithms for Cloud Visualization , 2007, ArXiv.