Abstract
The topics in scientific literature illustrate the contents of science domain, and the evolution of topics help in recognizing the research trend and front. Since the number of scientific works is growing exponentially, it is a great challenge for people to discover new research topics and topic changes. Fortunately, aided by text mining, visualization technologies are being widely used for topic analysis. Visualization is an effective tool for revealing the current status and topic evolution trend in a research field. Owing to the importance of topic analysis as well as the lack of a comprehensive description of this theme, we present a survey on the visualization methods for scientific literature topics. This paper introduces the basic concepts of bibliometrics and the pipeline of topic visualization. Based on the topic analysis tasks, we classify these papers into three categories: topic contents, topic relation, and topic evolution. Furthermore, each part is divided into smaller categories on the basis of the visual patterns. Some existing free software that integrates multiple functions are also introduced. Finally, we discuss the challenges and opportunities in the field of topic visualization.
Graphical Abstract
Similar content being viewed by others
References
Alencar AB, Oliveira MCFD, Paulovich FV (2012a) Seeing beyond reading: a survey on visual text analytics. Wiley Interdiscip Rev Data Min Knowl Discov 2(6):476–492
Alencar AB, Paulovich FV, Oliveira MCFD (2012b) Time-aware visualization of document collections. In: ACM symposium on applied computing, pp 997–1004
Alexander E, Gleicher M (2016) Task-driven comparison of topic models. IEEE Trans Vis Comput Graph 22(1):320–329
Alsakran J, Chen Y, Luo D, Zhao Y, Yang J, Dou W, Liu S (2012) Real-time visualization of streaming text with a force-based dynamic system. IEEE Comput Graph Appl 32(1):34
Blei DM (2012) Probabilistic topic models. Commun ACM 55(4):77–84
Blundell C, Teh YW, Heller KA (2010) Bayesian rose trees. In: UAI 2010, Proceedings of the twenty-sixth conference on uncertainty in artificial intelligence, Catalina Island, CA, USA, July, pp 65–72
Brner K (2010) Atlas of science: visualizing what we know. The MIT Press, Cambridge
Cao N, Sun J, Lin YR, Gotz D, Liu S, Qu H (2010) Facetatlas: multifaceted visualization for rich text corpora. IEEE Trans Vis Comput Graph 16(6):1172–1181
Chaney AJB, Blei DM (2012) Visualizing topic models. ICWSM 2012
Chen C (2006) CiteSpace II: detecting and visualizing emerging trends and transient patterns in scientific literature. J Am Soc Inf Sci Technol 57(3):359–377
Chen C (2013) Mapping scientific frontiers: the quest for knowledge visualization, 2nd edn
Chen C, Paul RJ (2001) Visualizing a knowledge domain’s intellectual structure. Computer 34(3):65–71
Choo J, Lee C, Reddy CK, Park H (2013) UTOPIAN: user-driven topic modeling based on interactive nonnegative matrix factorization. IEEE Trans Vis Comput Graph 19(12):1992
Chuang J, Manning CD, Heer J (2012a) Termite: visualization techniques for assessing textual topic models. In: International working conference on advanced visual interfaces, pp 74–77
Chuang J, Ramage D, Mcfarl DA, Manning CD, Heer J (2012b) Large-scale examination of academic publications using statistical models
Cobo MJ, López-Herrera AG, Herrera-Viedma E, Herrera F (2011) An approach for detecting, quantifying, and visualizing the evolution of a research field: a practical application to the fuzzy sets theory field. J Informetr 5(1):146–166
Collins C, Viegas FB, Wattenberg M (2009) Parallel tag clouds to explore and analyze faceted text corpora. In: IEEE symposium on visual analytics science and technology, 2009. VAST 2009, pp 91–98
Davidson GS, Hendrickson B, Johnson DK, Meyers CE, Wylie BN (1998) Knowledge mining with vxinsight: discovery through interaction. J Intell Inf Syst 11(3):259–285
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):20842097
Dou W, Wang X, Chang R, Ribarsky W (2011) Paralleltopics: a probabilistic approach to exploring document collections. In: Visual analytics science and technology, pp 231–240
Dou W, Yu L, Wang X, Ma Z, Ribarsky W (2013) Hierarchicaltopics: visually exploring large text collections using topic hierarchies. IEEE Trans Vis Comput Graph 19(12):2002–2011
Federico P, Heimerl F, Koch S, Miksch S (2017) A survey on visual approaches for analyzing scientific literature and patents. IEEE Trans Vis Comput Graph 23(9):2179–2198
Fried D, Kobourov SG (2013) Maps of computer science. In: Visualization symposium, pp 113–120
Gad S, Javed W, Ghani S, Elmqvist N, Ewing T, Hampton KN, Ramakrishnan N (2015) Themedelta: dynamic segmentations over temporal topic models. IEEE Trans Vis Comput Graph 21(5):672–85
Gretarsson B, Bostandjiev S, Asuncion A, Newman D, Smyth P (2012) TopicNets: visual analysis of large text corpora with topic modeling. ACM Trans Intell Syst Technol 3(2):23
Hascot M, Dragicevic P (2011) Visual comparison of document collections using multi-layered graphs. RR-11020, 2011, pp 1–10
Havre S, Hetzler B, Nowell L (2000) Themeriver: visualizing theme changes over time. In: Proceedings of the IEEE symposium on information visualization InfoVis, pp 115–115
Heimerl F, Han Q, Koch S, Ertl T (2016) CiteRivers: visual analytics of citation patterns. IEEE Trans Vis Comput Graph 22(1):190
Janssens FAL, Glänzel W, Moor BD (2007) Dynamic hybrid clustering of bioinformatics by incorporating text mining and citation analysis. In: ACM SIGKDD international conference on knowledge discovery and data mining, pp 360–369
Jiang X, Zhang J (2016) A text visualization method for cross-domain research topic mining. J Vis 19(3):561–576
Keena N, Etman MA, Draper J, Pinheiro P, Dyson A (2016) Interactive visualization for interdisciplinary research. Vis Data Anal 2016:1–7
Kohonen T, Kaski S, Lagus K, Salojärvi J, Honkela J, Paatero V, Saarela A (2000) Self organization of a massive document collection. IEEE Trans Neural Netw Learning Syst 11(3):574–585
Kucher K, Kerren A (2015) Text visualization techniques: taxonomy, visual survey, and community insights, In: PacificVis, pp 117–121
Lee B, Czerwinski M, Robertson G, Bederson BB (2005) Understanding research trends in conferences using PaperLens. In: Extended abstracts proceedings of the 2005 conference on human factors in computing systems, CHI 2005, Portland, Oregon, USA, April, pp 1969–1972
Lee H, Kihm J, Choo J, Stasko J, Park H (2012) iVisClustering: an interactive visual document clustering via topic modeling. Comput Graph Forum 31(3pt3):1155–1164
Liu S, Wang X, Song Y, Guo B (2015) Evolutionary Bayesian rose trees. IEEE Trans Knowl Data Eng 27(6):1533–1546
Maiya AS, Rolfe RM (2014) Topic similarity networks: visual analytics for large document sets. In: IEEE international conference on big data, pp 364–372
Mane KK, Brner K (2004) Mapping topics and topic bursts in PNAS. Proc Natl Acad Sci USA 101(Suppl 1):5287
Morris SA, Yen G, Wu Z, Asnake B (2003) Time line visualization of research fronts. J Am Soc Inf Sci Technol 54(5):413–422
Murdock J, Allen C (2015) Visualization techniques for topic model checking. In: AAAI conference on artificial intelligence, pp 4284–4285
Oelke D, Strobelt H, Rohrdantz C, Gurevych I, Deussen O (2014) Comparative exploration of document collections: a visual analytics approach. Comput Graph Forum 33(3):201–210
Oesterling P, Scheuermann G, Teresniak S, Heyer G, Koch S, Ertl T, Weber GH (2010) Two-stage framework for a topology-based projection and visualization of classified document collections. In: Visual analytics science and technology, pp 91–98
Skupin A (2002) A cartographic approach to visualizing conference abstracts. IEEE Comput Graph Appl 22(1):50–58
Skupin A (2004) The world of geography: visualizing a knowledge domain with cartographic means. Proc Natl Acad Sci USA 101(Supplement 1):5274
Small H (1973) Co citation in the scientific literature: a new measure of the relationship between two documents. J Am Soc Inf Sci 24(4):265–269
Wang X, Cheng Q, Lu W (2014) Analyzing evolution of research topics with NEViewer: a new method based on dynamic co-word networks. Scientometrics 101(2):1253–1271
Wang X, Liu S, Liu J, Chen J, Zhu J, Guo B (2016) TopicPanorama: a full picture of relevant topics. IEEE Trans Vis Comput Graph 22(12):2508
Wei F, Liu S, Song Y, Pan S, Zhou MX, Qian W, Shi L, Tan L, Zhang Q (2010) TIARA: a visual exploratory text analytic system. In: ACM SIGKDD international conference on knowledge discovery and data mining, Washington, DC, USA, July, pp 153–162
Wise JA (1999) The ecological approach to text visualization. Wiley, New York
Wu Y, Thomas P, Wei F, Liu S, Ma K (2011) Semantic-preserving word clouds by seam carving. Comput Graph Forum 30(3):741–750
Yan E, Ding Y (2012) Scholarly network similarities: how bibliographic coupling networks, citation networks, cocitation networks, topical networks, coauthorship networks, and coword networks relate to each other. Wiley, New York
Acknowledgements
This work was supported by Qinghai Science and technology Projects (No. 2016-ZJ-Y04).
Author information
Authors and Affiliations
Corresponding author
Rights and permissions
About this article
Cite this article
Zhang, C., Li, Z. & Zhang, J. A survey on visualization for scientific literature topics. J Vis 21, 321–335 (2018). https://doi.org/10.1007/s12650-017-0462-2
Received:
Accepted:
Published:
Issue Date:
DOI: https://doi.org/10.1007/s12650-017-0462-2