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.
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Blei DM, Lafferty JD (2007) A correlated topic model of science. Ann Appl Stat 1:17–35
Blei DM, Lafferty JD (2006) Dynamic topic models. In: Proceedings of the 23rd international conference on machine learning. ACM, pp 113–120
Blei DM, Ng AY, Jordan MI (2003) Latent dirichlet allocation. J Mach Learn Res 3:993–1022
Boyack KW (2004) Mapping knowledge domains: characterizing PNAS. Proc Natl Acad Sci 101(suppl 1):5192–5199
Chen C (2004) Searching for intellectual turning points: progressive knowledge domain visualization. Proc Natl Acad Sci 101(suppl 1):5303–5310
Chen C (2006) CiteSpace II: detecting and visualizing emerging trends and transient patterns in scientific literature. J Am Soc Inform Sci Technol 57(3):359–377
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):2412–2421
Deerwester SC, Dumais ST, Landauer TK et al (1990) Indexing by latent semantic analysis. JAsIs 41(6):391–407
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):2084–2097
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 231–240
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):2002–2011
Gad S, Javed W, Ghani S et al (2015) ThemeDelta: dynamic segmentations over temporal topic models. IEEE Trans Visual Comput Graphics 21(5):672–685
Ginsparg P, Houle P, Joachims T et al (2004) Mapping subsets of scholarly information. Proc Natl Acad Sci 101(suppl 1):5236–5240
Griffiths TL, Steyvers M (2004) Finding scientific topics. Proc Natl Acad Sci 101(suppl 1):5228–5235
Havre S, Hetzler E, Whitney P et al (2002) Themeriver: visualizing thematic changes in large document collections. IEEE Trans Vis Comput Graph 8(1):9–20
Heimerl F, Han Q, Koch S, Ertl T (2015) CiteRivers: visual analytics of citation patterns. IEEE Trans Visual Comput Graphics 22(1):190–199
Isenberg P, Isenberg T, Sedlmair M et al (2014) Toward a deeper understanding of visualization through keyword analysis. arXiv preprint arXiv:1408.3297
Landauer TK, Laham D, Derr M (2004) From paragraph to graph: latent semantic analysis for information visualization. Proc Natl Acad Sci 101(suppl 1):5214–5219
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 183–192
Liu S, Wu Y, Wei E et al (2013) StoryFlow: tracking the evolution of stories. IEEE Trans Vis Comput Graph 19(12):2436–2445
Mane KK, Börner K (2004) Mapping topics and topic bursts in PNAS. Proc Natl Acad Sci 101(suppl 1):5287–5290
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 633–640
Morris SA, Yen GG (2004) Crossmaps: visualization of overlapping relationships in collections of journal papers. Proc Natl Acad Sci 101(suppl 1):5291–5296
Newman MEJ (2004) Coauthorship networks and patterns of scientific collaboration. Proc Natl Acad Sci 101(suppl 1):5200–5205
Oelke D, Strobelt H, Rohrdantz C et al (2014) Comparative exploration of document collections: a visual analytics approach. Comput Graph Forum 33:201–210
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 248–256
Romo-Fernández LM, Guerrero-Bote VP, Moya-Anegón F (2013) Co-word based thematic analysis of renewable energy (1990–2010). Scientometrics 97(3):743–765
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 437–445
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 153–162
White HD, Lin X, Buzydlowski JW et al (2004) User-controlled mapping of significant literatures. Proc Natl Acad Sci 101(suppl 1):5297–5302
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):1763–1772
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Jiang, X., Zhang, J. A text visualization method for cross-domain research topic mining. J Vis 19, 561–576 (2016). https://doi.org/10.1007/s12650-015-0323-9
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DOI: https://doi.org/10.1007/s12650-015-0323-9