Research article Special Issues

Research on knowledge dissemination in smart cities environment based on intelligent analysis algorithms: a case study on online platform

  • Received: 06 January 2021 Accepted: 03 March 2021 Published: 19 March 2021
  • In developing smart cities, the implementation of social connections, collaboration, innovation, exchange of views by observing, exploiting and integrating various types of knowledge is required. The smart cities concept that employs knowledge sharing mechanism can be defined as the concept of a city that utilizes information technology to increase citizens' awareness, intelligence as well as community's participation. The knowledge dissemination via online sharing platforms has been becoming more popular in recent years, especially during the epidemic of infectious diseases. Thus, the social network and emotional analysis method based on intelligent data analysis algorithms is proposed to study the speaker relationship and comment sentiment tendency of a Chinese popular speech (knowledge dissemination) platform: YiXi. In our research, 690 speakers' information and 23,685 comments' information are collected from YiXi website as the data source. The speaker relationship network construction algorithm and emotional analysis algorithm are designed in details respectively. Experiments show that speakers who have the same profession can deliver different types of speeches, indicating that selection of YiXi platform in the invitation of speakers is diversified. In addition, overall sentiment tendency of comments on speeches seem to be slightly positive and most of them are the personal feelings according to their experience after watching speech videos instead of the direct evaluations of speech quality. The research aims to gain an insight into the popular knowledge sharing phenomenon and is expected to provide reference for knowledge dissemination platforms in order to improve the knowledge sharing environment in smart cities.

    Citation: Chengzhi Jiang, Hao Xu, Chuanfeng Huang, Yiyang Chen, Ruoqi Zou, Yixiu Wang. Research on knowledge dissemination in smart cities environment based on intelligent analysis algorithms: a case study on online platform[J]. Mathematical Biosciences and Engineering, 2021, 18(3): 2632-2653. doi: 10.3934/mbe.2021134

    Related Papers:

  • In developing smart cities, the implementation of social connections, collaboration, innovation, exchange of views by observing, exploiting and integrating various types of knowledge is required. The smart cities concept that employs knowledge sharing mechanism can be defined as the concept of a city that utilizes information technology to increase citizens' awareness, intelligence as well as community's participation. The knowledge dissemination via online sharing platforms has been becoming more popular in recent years, especially during the epidemic of infectious diseases. Thus, the social network and emotional analysis method based on intelligent data analysis algorithms is proposed to study the speaker relationship and comment sentiment tendency of a Chinese popular speech (knowledge dissemination) platform: YiXi. In our research, 690 speakers' information and 23,685 comments' information are collected from YiXi website as the data source. The speaker relationship network construction algorithm and emotional analysis algorithm are designed in details respectively. Experiments show that speakers who have the same profession can deliver different types of speeches, indicating that selection of YiXi platform in the invitation of speakers is diversified. In addition, overall sentiment tendency of comments on speeches seem to be slightly positive and most of them are the personal feelings according to their experience after watching speech videos instead of the direct evaluations of speech quality. The research aims to gain an insight into the popular knowledge sharing phenomenon and is expected to provide reference for knowledge dissemination platforms in order to improve the knowledge sharing environment in smart cities.



    加载中


    [1] S. A. Rani, M. A. Jabar, R. Abdullah, Y. Y. Jusoh, The Influence Factors of Knowledge Resilience in Sustaining Knowledge Network in Smart Cities Environment, 2020 6th International Conference on Information Management (ICIM), London, United Kingdom, 2020.
    [2] E. Negre, C. Rosenthal-Sabroux, M. Gascó, A Knowledge-Based Conceptual Vision of the Smart City, 2015 48th Hawaii International Conference on System Sciences, Kauai, 2015.
    [3] A. Elabora, M. Alkhatib, S. S. Mathew, M. El Barachi, Evaluating Citizens' Sentiments in Smart Cities: A Deep Learning Approach, 2020 5th International Conference on Smart and Sustainable Technologies (SpliTech), Split, Croatia, 2020.
    [4] Y. Cai, Y. Zhao, J. Yang, C. Wang, A Bus Passenger Flow Estimation Method Based on POI Data and AFC Data Fusion, ICBDS 2019, Communications in Computer and Information Science, 2019.
    [5] T. Sun, Y. Zhao, Z. Lian, People Flow Analysis Based on Anonymous OD Trip Data, ICBDS 2019, Communications in Computer and Information Science, 2019.
    [6] Q. Ye, J. Yang, F. Liu, C. Zhao, N. Ye, T. Yin, L1-Norm Distance Linear Discriminant Analysis Based on an Effective Iterative Algorithm, IEEE Transactions on Circuits and Systems for Video Technology, 2018.
    [7] Q. Ye, Z. Li, L. Fu, Z. Zhang, W. Yang, G. Yang, Nonpeaked discriminant analysis for data representation, IEEE Trans. Neural Networks Learn. Syst., 30 (2019), 3818–3832. doi: 10.1109/TNNLS.2019.2944869
    [8] L. Fu, Z. Li, Q. Ye, H. Yin, Q. Liu, X. Chen, et al., Learning robust discriminant subspace based on joint L2, p- and L2, s-Norm distance metrics, IEEE Trans. Neural Networks Learn. Syst., (2020), forthcoming.
    [9] J. Lin, Social Network Analysis: Theory, Method and Applications, First edition, Beijing Normal University Press, Beijing, 2009.
    [10] V. Colizza, A. Flammini, M. A. Serrano, A. Vespignani, Detecting rich-club ordering in complex networks, Nat. Phys., 2 (2006), 110–115. doi: 10.1038/nphys209
    [11] S. P. Borgatti, A. Mehra, D. J. Brass, G. Labianca, Network analysis in the social sciences, Science, 323 (2009), 892–895. doi: 10.1126/science.1165821
    [12] M. Girvan, M. E. J. Newman, Community structure in social and biological networks, PNAS, 99 (2002), 7821–7826. doi: 10.1073/pnas.122653799
    [13] M. E. J. Newman, Fast algorithm for detecting community structure in networks, Phys. Rev. E, 69 (2004), 066133. doi: 10.1103/PhysRevE.69.066133
    [14] V. D. Blondel, J. L. Guillaume, R. Lambiotte, E. Lefebvre, Fast unfolding of communities in large networks, J. Stat. Mech. Theory Exp., 2008 (2008), P10008.
    [15] L. Waltman, N. J. Van Eck, A smart local moving algorithm for large-scale modularity-based community detection, Eur. Phys. J. B, 86 (2013), 471.
    [16] L. Erhan, M. Ndubuaku, E. Ferrara, M. Richardson, D. Sheffield; F. J. Ferguson, et al., Analyzing objective and subjective data in social sciences: implications for smart cities, IEEE Access, 7 (2019), 19890–19906. doi: 10.1109/ACCESS.2019.2897217
    [17] C. Xu, Z. Guan, W. Zhao, Q. Wu, M. Yan, L. Chen, et al., Recommendation by users' multi-modal preferences for smart city applications, IEEE Trans. Ind. Inf., 17 (2021), 4197–4205. doi: 10.1109/TII.2020.3008923
    [18] N. Kilicay-Ergin, A. S. Barb, Smart City Document Evaluation to Support Policy Analysis, 2020 IEEE International Systems Conference (SysCon), Montreal, QC, 2020.
    [19] K. S. Oza, P. G. Naik, Prediction of online lectures popularity: a text mining approach, Procedia Comput. Sci., 92 (2016), 468–474. doi: 10.1016/j.procs.2016.07.369
    [20] A. Ghose, P. G. Ipeirotis, Designing Novel Review Ranking Systems: Predicting Usefulness and Impact of Reviews, International Conference on Electronic Commerce. ACM, 2007.
    [21] S. M. Mudambi, S. David, What makes a helpful online review? a study of customer reviews on Amazon.com, MIS Q., 34 (2010), 185–200.
    [22] J. Chen, J. Zhang, Y. Zhang, Impact factors of online customer reviews usefulness: a text semantics approach, Libr. Inf. Serv., 56 (2012), 119–123.
    [23] G. Yin, W. Liu, S. Zhu, What makes a helpful online review? The perspective of information adoption and social network, Libr. Inf. Ser., 56 (2012), 140–147.
    [24] A. Founoun, A. Hayar, A. Haqiq, The Textual Data Analysis Approach to Assist the Diagnosis of Smart Cities Initiatives, 2019 IEEE International Smart Cities Conference (ISC2), Casablanca, Morocco, 2019.
    [25] H. Yu, Z. Li., Y. Jiang, Using GitHub Open Sources and Database Methods Designed to Auto-Generate Chinese Tang Dynasty Poetry, Communications in Computer and Information Science, 2020.
    [26] M. Zhang, Sentiment analysis of E-commerce reviews based on text mining, Ind. Sci. Tribune, 19 (2020), 63–64.
    [27] X. Li, W. Yu, Text mining of comment dada on video electronic product based on sentiment analysis and relational network, Intell. Explor., 2018 (2018), 1–5.
    [28] X. Wu, X. Li, W. Zhao, Research on cultural landscape patterns of Guanzhong area based on text mining of Tang poetry, Landscape Archit., 26 (2019), 52–57.
    [29] T. M. Fruchterman, E. M. Reingold, Graph drawing by force-directed placement, Software Pract. Exp., 21 (1991), 1129–1164. doi: 10.1002/spe.4380211102
    [30] U. Brandes, A faster algorithm for betweenness centrality, J. Math. Soc., 25 (2001), 163–177. doi: 10.1080/0022250X.2001.9990249
    [31] V. D Blondel, J. Guillaume, R. Lambiotte, E. Lefebvre, Fast unfolding of communities in large networks, J. Stat. Mech. Theory Exp., 2008 (2008), P1000.
    [32] R. Lambiotte, J. C. Delvenne, M. Barahona, Laplacian dynamics and multiscale modular structure in networks, preprint, arXiv: 0812.1770.
    [33] R. Tarjan, Depth-first search and linear graph algorithms, SIAM J. Comput., 1 (1972), 146–160. doi: 10.1137/0201010
    [34] M. Latapy, Main-memory triangle computations for very large (Sparse (Power-Law)) graphs, Theor. Comput. Sci., 407 (2008), 458–473. doi: 10.1016/j.tcs.2008.07.017
    [35] L. Xu, H. Lin, Y. Pan, H. Ren, J. Chen, Constructing the affective lexicon ontology, J. China Soc. Sci. Tech. Inf., 27 (2008), 180–185.
    [36] Collection of words for sentiment analysis (beta version), 2020. Available from: http://www.keenage.com/html/c_bulletin_2007.htm.
    [37] G. Qin, S. Deng, H. Wang, Chinese stopwords for text clustering: a comparative study, Data Anal. Knowl. Discovery, 1 (2017), 72–80.
    [38] Ping An Zhiniao Education Platform, 2020. Available from: https://www.zhi-niao.com/platform.html#.
  • Reader Comments
  • © 2021 the Author(s), licensee AIMS Press. This is an open access article distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/by/4.0)
通讯作者: 陈斌, bchen63@163.com
  • 1. 

    沈阳化工大学材料科学与工程学院 沈阳 110142

  1. 本站搜索
  2. 百度学术搜索
  3. 万方数据库搜索
  4. CNKI搜索

Metrics

Article views(3127) PDF downloads(144) Cited by(0)

Article outline

Figures and Tables

Figures(7)  /  Tables(9)

/

DownLoad:  Full-Size Img  PowerPoint
Return
Return

Catalog