Abstract
“Energy” has been one element of the development of human civilization, also a power for national industry, construction and economic development. The green energy has become the cornerstone in sustainable development to secure such energy supply but may accommodate opinions from controversial perspectives when this subject is discussed. This study develops an interactive big data system, which aims at aggregating data from Facebook, PTT, news, and provides an interactive interface for energy domain experts. The “interaction” characterizes the seamless integration between users and the system to construct the controversial issue sets of energy, which could be identified and established autonomously in this study. The approach using tags of the link in two controversial issues can help end-users effectively query on demand. The energy relevant issues can be fully aware and provided to the decision makers from the positive and negative viewpoints.
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References
Bello-Orgaz, G., Jung, J.J., Camacho, D.: Social big data: recent achievements and new challenges. Inf. Fusion 28, 45–59 (2016)
Sutcliffe, K.M., Vogus, T.J.: Organizing for resilience. Posit. Organ. Scholarsh.: Found. New discipl. 94, 110 (2003)
Cobb, R.W.: Participation in American politics: The Dynamics of Agenda-Building. Johns Hopkins University Press, Baltimore (1983)
Bonsón, E., Torres, L., Royo, S., Flores, F.: Local e-government 2.0: social media and corporate transparency in municipalities. Gov. Inf. Q. 29(2), 123–132 (2012)
Cambria, E., Rajagopal, D., Olsher, D., Das, D.: Big social data analysis. In: Akerkar, R. (ed.) Big Data Computing, pp. 401-414. Chapman and Hall/CRC publication (2013)
Manovich, L.: Trending: the promises and the challenges of big social data. In: Debates in the Digital Humanities, pp. 460–447 (2011)
Young, S.D.: Behavioral insights on big data: using social media for predicting biomedical outcomes. Trends Microbiol. 22(11), 601–602 (2014)
Nguyen, T.H., Shirai, K., Velcin, J.: Sentiment analysis on social media for stock movement prediction. Expert Syst. Appl. 42(24), 9603–9961 (2015)
Jang, H.J., Sim, J., Lee, Y., Kwon, O.: Deep sentiment analysis: mining the causality between personality-value-attitude for analyzing business ads in social media. Expert Syst. Appl. 40(18), 7492–7503 (2013)
White, T.: Hadoop: The Definitive Guide. O’Reilly Media, Sebastopol (2009)
Zaharia, M., Chowdhury, M., Franklin, M.J., Shenker, S., Stoica, I.: Spark: cluster computing with working sets. In: Proceedings of 2nd USENIX Conference on Hot Topics in Cloud Computing, vol. 10, p. 10 (2010)
Anil, R., Dunning, T., Friedman, E.: Mahout in Action, pp. 1–2. Manning Publications Co., New York (2011)
Meng, X., Bradley, J., Yavuz, B., Sparks, E., Venkataraman, S., Liu, D., Tal-Walkar, A.: MLlib: Machine Learning in Apache Spark (2015). arXiv preprint arXiv:1505.06807
Bekkerman, R.: Automatic categorization of email into folders: benchmark experiments on Enron and SRI corpora (2004)
Mei, Q., Liu, C., Su, H., Zhai, C.: A probabilistic approach to spatiotemporal theme pattern mining on weblogs. In: Proceedings of 15th International Conference on World Wide Web, pp. 533–542 (2006)
Allan, J., Carbonell, J.G., Doddington, G., Yamron, J., Yang, Y.: Topic detection and tracking pilot study final report (1998)
Zhao, Q., Mitra, P.: Event detection and visualization for social text streams. In: ICWSM (2007)
Krause, A., Leskovec, J., Guestrin, C.: Data association for topic intensity tracking. In: Proceedings of 23rd International Conference on Machine Learning. pp. 497–504. ACM (2006)
Weng, J., Lee, B.S.: Event detection in Twitter. In: ICWSM, vol. 11, pp. 401–408 (2011)
Li, R., Lei, K.H., Khadiwala, R., Chang, K.C.C.: Tedas: a Twitter-based event detection and analysis system. In: 2012 IEEE 28th International Conference on Data Engineering (ICDE). pp. 1273–1276. IEEE (2012)
Miller, J.A., Potter, W.D., Kochut, K.J.: Knowledge, data, and models: taking an objective orientation on integrating these three. IEEE Potentials 11(4), 13–17 (1992)
Inmon, W.H., Zachman, J.A., Geiger, J.G.: Data Stores, Data Ware-Housing, and the Zachman Framework: Managing Enterprise Knowledge. McGraw-Hill Companies, Inc., New York (1997)
Huang, C.C., Kuo, C.M.: Transformation and searching of semi-structured knowledge in organizations. J. Knowl. Manag. 7(4), 106–123 (2003)
Chase, W.H.: Public issue management: the new science. Publ. Relat. J. 33(10), 25–26 (1977)
Kong, Q., Mao, W., Zeng, D., Wang, L.: Predicting popularity of forum threads for public events security. In: 2014 IEEE Joint Intelligence and Security Informatics Conference (JISIC), pp. 99–106 (2014)
Chen, C.C., Chen, Y.-T., Sun, Y., Chen, M.-C.: Life cycle modeling of news events using aging theory. In: Lavrač, N., Gamberger, D., Todorovski, L., Blockeel, H. (eds.) ECML 2003. LNCS (LNAI), vol. 2837, pp. 47–59. Springer, Heidelberg (2003)
Chen, C.C., Chen, Y.T., Chen, M.C.: An aging theory for event life-cycle modeling. IEEE Trans. Syst. Man Cybern. Part A Syst. Hum. 37(2), 237–248 (2007)
Kim, H.G., Lee, S., Kyeong, S.: Discovering hot topics using Twitter streaming data social topic detection and geographic clustering. In: 2013 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining (ASONAM), pp. 1215–1220. IEEE (2013)
Kuo, B.Y., Hentrich, T., Good, B.M., Wilkinson, M.D.: Tag clouds for summarizing web search results. In: Proceedings of 16th International Conference on World Wide Web, pp. 1203–1204. ACM (2007)
Koutrika, G., Zadeh, Z.M., Garcia-Molina, H.: Data clouds: summarizing keyword search results over structured data. In: Proceedings of 12th International Conference on Extending Database Technology: Advances in Database Technology, pp. 391–402. ACM (2009)
Nonaka, I., Konno, N.: The concept of “B, A”: building a foundation for knowledge creation. Knowl. Manag.: Crit. Perspect. Bus. Manag. 2(3), 53 (2005)
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Huang, CC., Fang, YJ., Lin, SH., Liang, WY., Wu, SR. (2016). Development of Issue Sets from Social Big Data: A Case Study of Green Energy and Low-Carbon. In: Perner, P. (eds) Advances in Data Mining. Applications and Theoretical Aspects. ICDM 2016. Lecture Notes in Computer Science(), vol 9728. Springer, Cham. https://doi.org/10.1007/978-3-319-41561-1_11
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