This article presents analysis of data from scientific articles and patents to identify the evolving trends and underlying topics in research on humanoid robots. We used topic modelling based on latent Dirichlet allocation analysis to identify underlying topics in sub-areas in the field. We also used social network analysis to measure the centrality indices of publication keywords to detect important and influential sub-areas and used co-occurrence analysis of keywords to visualise relationships among subfields. The research result is useful to identify evolving topics and areas of current focus in the field of humanoid technology. The results contribute to identify valuable research patterns from publications and to increase understanding of the hidden knowledge themes that are revealed by patents.
ZhaoS.Humanoid social robots as a medium of communication. New Media Soc2006; 8: 401–419.
6.
MejiaCKajikawaY.Bibliometric analysis of social robotics research: identifying research trends and knowledgebase. Appl Sci2017; 7: 1316.
7.
GoeldnerMHerstattCTietzeF.The emergence of care robotics – a patent and publication analysis. Technol Forecast Soc2015; 92: 115–131.
8.
AltuntasSDereliSKusiakA.Forecasting technology success based on patent data. Technol Forecast Soc2015; 96: 202–214.
9.
KimGJParkSSJangDS.Technology forecasting using topic-based patent analysis. J Sci Ind Res2015; 74: 265–270.
10.
MadaniFWeberC.The evolution of patent mining: applying bibliometric analysis and keyword network analysis. World Patent Inform2016; 46: 32–48.
11.
ParkJKBaeYC.Scientometric analysis for pilot study of smart grid. J Korea Inst Electron Commun Sci2012; 7: 97–105.
12.
DurackKT.Tacit knowledge in patent applications: observations on the value of models to early US Patent Office practice and potential implications for the 21st century. World Patent Inform2004; 26(2): 131–136.
13.
DaimTURuedaGMartinHet al. Forecasting emerging technologies: use of bibliometric and patent analysis. Technol Forecast Soc2006; 73: 981–1012.
14.
ChenHJiangWYangYet al. A bibliometric analysis of waste management research during the period 1997–2014. Scientometrics2015; 105: 1005–1018.
15.
CôrtesPLRodriguesR.A bibliometric study on ‘education for sustainability’. Braz J Sci Tech2016; 3: 3–8.
BraamRRMoedHFVan RaanAFJ. Mapping of Science by combined co-citation and word analysis I: structural aspects. J Am Soc Inform Sci1991; 42: 233–251.
18.
HeQ.Knowledge discovery through co-word analysis. Libr Trends1999; 48: 133–159.
19.
RadhakrishnanSErbisSIssacsJAet al. Novel keyword co-occurrence network-based methods to foster systematic reviews of scientific literature. PLoS ONE2017; 12(3): e0172778.
20.
LeeBJeongYI.Mapping Korea’s national R&D domain of robot technology by using the co-word analysis. Scientometrics2008; 77: 3–19.
21.
SedighiMJalalimaneshA.Mapping research trends in the field of knowledge management. Malays J Libr Inf Sc2014; 19: 71–85.
22.
QuoniamLHassanalyPBalditPet al. Bibliometric analysis of patent documents for R&D management. Res Evaluat1993; 3(1): 13–18.
23.
SuominenAToivanenHSeppänenM.Firms’ knowledge profiles: mapping patent data with unsupervised learning. Technol Forecast Soc2017; 115: 131–142.
24.
VenugopalanSRaiV.Topic based classification and pattern identification in patents. Technol Forecast Soc2015; 94: 236–250.
25.
NieBSunS.Using text mining techniques to identify research trends: a case study of design research. Appl Sci2017; 7: 401.
26.
TsengYHLinCHLinYI.Text mining techniques for patent analysis. Inform Process Manag2007; 43: 1216–1247.
27.
HuangMHYangHW.A scientometric study of fuel cell based on paper and patent analysis. J Libr Inf Stud2013; 11(2): 1–24.
28.
ChadeganiAASalehiHYunusMMet al. A comparison between two main academic literature collections: Web of Science and Scopus databases. Asian Social Sci2013; 9: 18–26.
29.
GuzANRushchitskyJJ.Scopus: a system for the evaluation of scientific journals. Int Appl Mech2009; 45: 351–362.
KimJLeeJKimGet al. A hybrid method of analyzing patents for sustainable technology management in humanoid robot industry. Sustainability2016; 8: 474.
32.
GrunBHornikK.Topic models: an R package for fitting topic models. J Stat Softw2011; 40(13): 24619.
33.
EckNJVWaltmanL. Software survey: VOSviewer, a computer program for bibliometric mapping. Scientometrics2010; 84: 523–538.
34.
HoppenNHFVanzSADS. Neurosciences in Brazil: a bibliometric study of main characteristics collaboration and citations. Scientometrics2016; 109: 121–141.
35.
MaoGLiuXDuHet al. Way forward for alternative energy research: a bibliometric analysis during 1994–2013. Renew Sust Energ Rev2015; 48: 276–286.
36.
RuedaGGerdsriPKocaogluDF. Bibliometrics and social network analysis of the nanotechnology field. In: Portland international conference on management of engineering & technology, Portland, OR, 5–9 August 2007, pp. 2905–2911. New York: IEEE.
37.
JeongJYKangIChoiKSet al. Network analysis on green technology in national research and development projects in Korea. Sustainability2018; 10: 1043.
38.
LeeS.Network analysis methodology. Seoul, South Korea: Nonhyung, 2012.
39.
FreemanLC.Centrality in social networks conceptual clarification. Soc Networks1979; 1: 215–239.
40.
GuptaNNarainAAroraAet al. Correlating centralities of social networks. In: IEEE international conference on advanced networks and telecommunications systems (ANTS), Bangalore, India, 6–9 November 2016. New York: IEEE.
41.
JunS. IPC code analysis of patent documents using association rules and maps – patent analysis of database technology. In: KimTAdeliHCuzzocreaAet al. (eds) Database theory and application, bio-science and bio-technology (Communications in Computer and Information Science). Berlin; Heidelberg: Springer, 2011, pp. 21–30.
LeeWJLeeWKSohnSY.Patent network analysis and quadratic assignment procedures to identify the convergence of robot technologies. PLoS ONE2016; 11: e0165091.
44.
SolisJTakanishiA.Recent trends in humanoid robotics research: scientific background, applications, and implications. Account Res2010; 17: 278–298.
45.
FennJRaskinoM.Mastering the hype cycle: how to choose the right innovation at the right time. Brighton, MA: Harvard Business Press, 2008.
46.
AdamutheACTomkeJVThampiGT.An empirical analysis of hype-cycle: a case study of cloud computing technologies. Int J Adv Res Comp Commun Eng2015; 4: 316–323.
47.
JarvenpaaHMakinenSJ. Empirically detecting the Hype Cycle with the life cycle indicators: an exploratory analysis of three technologies. In: 2008 IEEE international conference on industrial engineering and engineering management, Singapore, 8–11 December 2008, pp. 12–16. New York: IEEE.
48.
JunSP.An empirical study of users ‘hype cycle based on search traffic: the case study on hybrid cars’. Scientometrics2012; 91(1): 81–99.
