Interactive Visualization of Scholar Text

Conference paper
Part of the Lecture Notes in Electrical Engineering book series (LNEE, volume 269)

Abstract

Text visualization method depends on the contents of documents to analyze patterns and abstract characters. Words set or semantic relationships often get involved. However, visualizing the large scholar text as an understandable view for users is a challenging. We propose an interactive model to describe the scholar information by statistical work and clustering results. The users’ diverse interests are concerned by customizing the parameters. And the interface is designed to access data easily. Our conception comes from our experiences designing the Scholar Browser for a university which displays the contribution of departments and topic similarity between them.

Keywords

Information visualization Scholar text Statistical model 

References

  1. 1.
    Blei DM, Ng AY, Jordan MI (2003) Latent Dirichlet allocation. J Mach Learn Res 3:993–1022MATHGoogle Scholar
  2. 2.
    Chang J, Boyd-Graber J, Wang C, Gerrish S, and Blei DM (2009) Reading tea leaves: how humans interpret topic models. In: NIPS, pp 288–296Google Scholar
  3. 3.
    Chuang J, Ramage D, Manning C, et al. (2012) Interpretation and trust: designing model-driven visualizations for text analysis[C]. In: Proceedings of the 2012 ACM annual conference on human factors in computing systems. ACM, pp 443–452Google Scholar
  4. 4.
    Clough PD, Sen BA (2008) Evaluating tagclouds for health-related information research. In: Health Info Management Research Google Scholar
  5. 5.
    Collins C, Carpendale S, Penn G (2009) DocuBurst: visualizing document content using language structure. Comput Graph Forum 28:3CrossRefGoogle Scholar
  6. 6.
    Collins C, Viegas FB, Wattenberg M (2009) Parallel tag clouds to explore and analyze faceted text corpora. In: VAST, pp 91–98Google Scholar
  7. 7.
    Cutting DR., Karger, DR, Pedersen JO (1993) Constant interaction-time scatter/gather browsing of very large document collections. In: SIGIR Google Scholar
  8. 8.
    Hall D, Jurafsky D, Manning CD (2008) Studying the history of ideas using topic models. In: EMNLP, pp 363–371Google Scholar
  9. 9.
    Morrison JB, Tversky B, Betrancourt M (2000) Animation: does it facilitate learning? In: Proceedings of smart graphics AAAI Spring Symposium, pp 53–60. AAAI Press Technical Report SS-00-04Google Scholar
  10. 10.
    Robertson GG, Card SK, Mackinlay JD (1993) Information visualization using 3D interactive animation. Commun ACM 36(4):57–71CrossRefGoogle Scholar
  11. 11.
    Salton G, Wong A, Yang C (1975) A vector space model for automatic indexing. Commun ACM 18(11):613–620CrossRefMATHGoogle Scholar
  12. 12.
    Thomas J, Cook K, Eds (2005) Illuminating the path: the research and development agenda for visual analytics. IEEE Press, Los AlamitosGoogle Scholar
  13. 13.
    Viegas FB, Wattenberg M (2008) TIMELINES: tag clouds and the case for vernacular visualization. Interactions 15:49–52CrossRefGoogle Scholar
  14. 14.
    Wattenberg M, Viegas FB (2008) The word tree, an interactive visual concordance. In InfoVis, pp 1221–1228Google Scholar

Copyright information

© Springer Science+Business Media Dordrecht 2014

Authors and Affiliations

  1. 1.The department of Computer Science and TechnologyShandong UniversityShandongChina

Personalised recommendations