Advertisement

Journal of Visualization

, Volume 19, Issue 3, pp 561–576 | Cite as

A text visualization method for cross-domain research topic mining

  • Xinyi Jiang
  • Jiawan Zhang
Regular Paper

Abstract

Cross-domain research topic mining can help users find relationships among related research domains and obtain a quick overview of these domains. This study investigates the evolution of cross-domain topics of three interdisciplinary research domains and uses a visual analytic approach to determine unique topics for each domain. This study also focuses on topic evolution over 10 years and on individual topics of cross domains. A hierarchical topic model is adopted to extract topics of three different domains and to correlate the extracted topics. A simple yet effective visualization interface is then designed, and certain interaction operations are provided to help users more deeply understand the visualization development trend and the correlation among the three domains. Finally, a case study is conducted to demonstrate the effectiveness of the proposed method.

Graphical Abstract

Keywords

Topic mining Text visualization Visual analysis 

References

  1. Blei DM, Lafferty JD (2007) A correlated topic model of science. Ann Appl Stat 1:17–35MathSciNetCrossRefzbMATHGoogle Scholar
  2. Blei DM, Lafferty JD (2006) Dynamic topic models. In: Proceedings of the 23rd international conference on machine learning. ACM, pp 113–120Google Scholar
  3. Blei DM, Ng AY, Jordan MI (2003) Latent dirichlet allocation. J Mach Learn Res 3:993–1022zbMATHGoogle Scholar
  4. Boyack KW (2004) Mapping knowledge domains: characterizing PNAS. Proc Natl Acad Sci 101(suppl 1):5192–5199CrossRefGoogle Scholar
  5. Chen C (2004) Searching for intellectual turning points: progressive knowledge domain visualization. Proc Natl Acad Sci 101(suppl 1):5303–5310CrossRefGoogle Scholar
  6. Chen C (2006) CiteSpace II: detecting and visualizing emerging trends and transient patterns in scientific literature. J Am Soc Inform Sci Technol 57(3):359–377CrossRefGoogle Scholar
  7. Cui W, Liu S, Tan L et al (2011) Textflow: towards better understanding of evolving topics in text. IEEE Trans Vis Comput Graph 17(12):2412–2421CrossRefGoogle Scholar
  8. Deerwester SC, Dumais ST, Landauer TK et al (1990) Indexing by latent semantic analysis. JAsIs 41(6):391–407CrossRefGoogle Scholar
  9. Ding W, Chen C (2014) Dynamic topic detection and tracking: a comparison of HDP, C-word, and cocitation methods. J Assoc Inf Sci Technol 65(10):2084–2097CrossRefGoogle Scholar
  10. Dou W, Wang X, Chang R et al (2011) Paralleltopics: a probabilistic approach to exploring document collections/visual analytics science and technology (VAST). In: IEEE conference on 2011, pp 231–240Google Scholar
  11. Dou W, Li Y, Wang X et al (2013) HierarchicalTopics: visually exploring large text collections using topic hierarchies. IEEE Trans Vis Comput Graph 19(12):2002–2011CrossRefGoogle Scholar
  12. Gad S, Javed W, Ghani S et al (2015) ThemeDelta: dynamic segmentations over temporal topic models. IEEE Trans Visual Comput Graphics 21(5):672–685CrossRefGoogle Scholar
  13. Ginsparg P, Houle P, Joachims T et al (2004) Mapping subsets of scholarly information. Proc Natl Acad Sci 101(suppl 1):5236–5240CrossRefGoogle Scholar
  14. Griffiths TL, Steyvers M (2004) Finding scientific topics. Proc Natl Acad Sci 101(suppl 1):5228–5235CrossRefGoogle Scholar
  15. Havre S, Hetzler E, Whitney P et al (2002) Themeriver: visualizing thematic changes in large document collections. IEEE Trans Vis Comput Graph 8(1):9–20CrossRefGoogle Scholar
  16. Heimerl F, Han Q, Koch S, Ertl T (2015) CiteRivers: visual analytics of citation patterns. IEEE Trans Visual Comput Graphics 22(1):190–199CrossRefGoogle Scholar
  17. Isenberg P, Isenberg T, Sedlmair M et al (2014) Toward a deeper understanding of visualization through keyword analysis. arXiv preprint arXiv:1408.3297
  18. Landauer TK, Laham D, Derr M (2004) From paragraph to graph: latent semantic analysis for information visualization. Proc Natl Acad Sci 101(suppl 1):5214–5219CrossRefGoogle Scholar
  19. Liu S, Wang X, Chen J et al (2014) TopicPanorama: a full picture of relevant topics/visual analytics science and technology (VAST). In: IEEE conference on 2014, pp 183–192Google Scholar
  20. Liu S, Wu Y, Wei E et al (2013) StoryFlow: tracking the evolution of stories. IEEE Trans Vis Comput Graph 19(12):2436–2445CrossRefGoogle Scholar
  21. Mane KK, Börner K (2004) Mapping topics and topic bursts in PNAS. Proc Natl Acad Sci 101(suppl 1):5287–5290CrossRefGoogle Scholar
  22. Mimno D, Li W, McCallum A (2007) Mixtures of hierarchical topics with pachinko allocation. In: Proceedings of the 24th international conference on machine learning. ACM, pp 633–640Google Scholar
  23. Morris SA, Yen GG (2004) Crossmaps: visualization of overlapping relationships in collections of journal papers. Proc Natl Acad Sci 101(suppl 1):5291–5296CrossRefGoogle Scholar
  24. Newman MEJ (2004) Coauthorship networks and patterns of scientific collaboration. Proc Natl Acad Sci 101(suppl 1):5200–5205CrossRefGoogle Scholar
  25. Oelke D, Strobelt H, Rohrdantz C et al (2014) Comparative exploration of document collections: a visual analytics approach. Comput Graph Forum 33:201–210CrossRefGoogle Scholar
  26. Ramage D, Hall D, Nallapati R et al (2009) Labeled LDA: a supervised topic model for credit attribution in multi-labeled corpora. In: Proceedings of the 2009 conference on empirical methods in natural language processing, vol. 1. Association for Computational Linguistics, pp 248–256Google Scholar
  27. Romo-Fernández LM, Guerrero-Bote VP, Moya-Anegón F (2013) Co-word based thematic analysis of renewable energy (1990–2010). Scientometrics 97(3):743–765CrossRefGoogle Scholar
  28. Wang C, Danilevsky M, Desai N et al (2013) A phrase mining framework for recursive construction of a topical hierarchy. In: Proceedings of the 19th ACM SIGKDD international conference on knowledge discovery and data mining. ACM, pp 437–445Google Scholar
  29. Wei F, Liu S, Song Y et al (2010) Tiara: a visual exploratory text analytic system. In: Proceedings of the 16th ACM SIGKDD international conference on knowledge discovery and data mining. ACM, pp 153–162Google Scholar
  30. White HD, Lin X, Buzydlowski JW et al (2004) User-controlled mapping of significant literatures. Proc Natl Acad Sci 101(suppl 1):5297–5302CrossRefGoogle Scholar
  31. Wu Y, Liu S, Yan K et al (2014) OpinionFlow: visual analysis of opinion diffusion on social media. IEEE Trans Vis Comput Graph 20(12):1763–1772MathSciNetCrossRefGoogle Scholar

Copyright information

© The Visualization Society of Japan 2015

Authors and Affiliations

  1. 1.Tianjin UniversityTianjinChina

Personalised recommendations