Abstract
We all know that the ratio of social data noise is pretty significant. Therefore, tackling with noise problem is always obtained attention from data scientists. In this paper, we present a research of using reconstruction independent component analysis algorithm for blind separation social event signals from their mixtures (i.e., mixture is the combination of source signal and noise). This issue can be categorized as cocktail party problem. Despite cocktail party problem is a classical topic, however, dealing with social media data can be considered as a new research trend. From the case study with two events on Twitter, we demonstrate that our approach is quite promising. Further, our work can be applied for recommendation systems, or is used as a pre-processing step for other studies (e.g., focus search and event detection).
This is a preview of subscription content, log in via an institution.
Buying options
Tax calculation will be finalised at checkout
Purchases are for personal use only
Learn about institutional subscriptionsReferences
Anemüller, J., Sejnowski, T.J., Makeig, S.: Complex independent component analysis of frequency-domain electroencephalographic data. Neural Netw. 16(9), 1311–1323 (2003)
Becker, H., Naaman, M., Gravano, L.: Selecting quality twitter content for events. In: Proceedings of the 5th International Conference on Weblogs and Social Media (ICWSM 2011), Barcelona, Catalonia, Spain, 17–21 July 2011, pp. 442–445. AAAI Press (2011)
Benson, E., Haghighi, A., Barzilay, R.: Event discovery in social media feeds. In: Proceedings of the 49th Annual Meeting of the Association for Computational Linguistics: Human Language Technologies (ACL HLT 2011), Portland, Oregon, USA, 19–24 June 2011, pp. 389–398. ACL (2011)
Cruces-Alvarez, S., Cichocki, A., Castedo-Ribas, L.: An iterative inversion approach to blind source separation. IEEE Trans. Neural Netw. 11(6), 1423–1437 (2000)
Cui, A., Zhang, M., Liu, Y., Ma, S., Zhang, K.: Discover breaking events with popular hashtags in Twitter. In: Proceedings of the 21st ACM International Conference on Information and Knowledge Management (CIKM 2012), Maui, HI, USA, 29 October–02 November 2012, pp. 1794–1798. ACM (2012)
Diamantaras, K.I., Papadimitriou, T.: MIMO blind deconvolution using subspace-based filter deflation. In: Proceedings of the 29th International Conference on Acoustics, Speech, and Signal Processing (ICASSP 2004), Montreal, Quebec, Canada, 17–21 May 2004, pp. 433–436. IEEE (2004)
Ding, W.: A new method for image noise removal using chaos-PSO and nonlinear ICA. Procedia Eng. 24, 111–115 (2011)
Feng, W., et al.: STREAMCUBE: hierarchical spatio-temporal hashtag clustering for event exploration over the Twitter stream. In: Proceedings of the 31st IEEE International Conference on Data Engineering (ICDE 2015), Seoul, South Korea, 13–17 April 2015, pp. 1561–1572. IEEE (2015)
Fung, G.P.C., Yu, J.X., Yu, P.S., Lu, H.: Parameter free bursty events detection in text streams. In: Proceedings of the 31st International Conference on Very Large Data Bases (VLDB 2005), Trondheim, Norway, 30 August–02 September 2005, pp. 181–192. ACM (2005)
Hoang Long, N., Jung, J.J.: Privacy-aware framework for matching online social identities in multiple social networking services. Cybern. Syst. 46(1–2), 69–83 (2015)
Kim, C.M., Park, H.M., Kim, T., Choi, Y.K., Lee, S.Y.: FPGA implementation of ICA algorithm for blind signal separation and adaptive noise canceling. IEEE Trans. Neural Netw. 14(5), 1038–1046 (2003)
Le, Q.V., Karpenko, A., Ngiam, J., Ng, A.Y.: ICA with reconstruction cost for efficient overcomplete feature learning. In: Proceedings of the 24th Annual Conference on Advances in Neural Information Processing Systems (NIPS 2011), Granada, Spain, 12–14 December 2011, pp. 1017–1025. Curran Associates, Inc. (2011)
Li, Y., Ma, Z., Lu, W., Li, Y.: Automatic removal of the eye blink artifact from EEG using an ICA-based template matching approach. Physiol. Measur. 27(4), 425 (2006)
Nguyen, H.L., Jung, J.E.: Statistical approach for figurative sentiment analysis on social networking services: a case study on Twitter. Multimed. Tools Appl. 76(6), 8901–8914 (2017)
Nguyen, H.L., Jung, J.E.: SocioScope: a framework for understanding internet of social knowledge. Future Gener. Comput. Syst. 83, 358–365 (2018)
Nuzillard, D., Bijaoui, A.: Blind source separation and analysis of multispectral astronomical images. Astron. Astrophys. Suppl. Ser. 147(1), 129–138 (2000)
Petrović, S., Osborne, M., Lavrenko, V.: Streaming first story detection with application to Twitter. In: Proceedings of the 11th International Conference of the North American Chapter of the Association for Computational Linguistics (NAACL 2010), Los Angeles, California, USA, 2–4 June 2010, pp. 181–189. ACL (2010)
Phuvipadawat, S., Murata, T.: Breaking news detection and tracking in Twitter. In: Proceedings of the International Conference on Web Intelligence and International Conference on Intelligent Agent Technology (WI-IAT 2010), Toronto, Canada, 31 August–03 September 2010, pp. 120–123. IEEE (2010)
Sakaki, T., Okazaki, M., Matsuo, Y.: Earthquake shakes Twitter users: real-time event detection by social sensors. In: Proceedings of the 19th International Conference on World Wide Web (WWW 2010), Raleigh, North Carolina, USA, 26–30 April 2010, pp. 851–860. ACM (2010)
Smith, E.A.: The role of tacit and explicit knowledge in the workplace. J. Knowl. Manag. 5(4), 311–321 (2001)
Vorobyov, S., Cichocki, A.: Blind noise reduction for multisensory signals using ICA and subspace filtering, with application to EEG analysis. Biol. Cybern. 86(4), 293–303 (2002)
Wright, S., Nocedal, J.: Numerical optimization. Springer Sci. 35(67–68), 7 (1999)
Yang, Y., Pierce, T., Carbonell, J.: A study of retrospective and on-line event detection. In: Proceedings of the 21st International Conference on Research and Development in Information Retrieval (SIGIR 1998), Melbourne, Australia, 24–28 August 1998, pp. 28–36. ACM (1998)
Acknowledgment
This research was supported by the MIST (Ministry of Science and ICT), Korea, under the National Program for Excellence in SW supervised by the IITP (Institute for Information & communications Technology Promotion) (20170001000021001). Also, this work was supported by the National Research Foundation of Korea (NRF) grant funded by the Korea government (MSIP) (NRF-2017R1A4A1015675, 2017R1A2B4010774).
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2018 Springer Nature Switzerland AG
About this paper
Cite this paper
Nguyen, H.L., Bui, KH.N., Jo, N., Jung, J.J., Camacho, D. (2018). Towards Social Signal Separation Based on Reconstruction Independent Component Analysis. In: Xiang, Y., Sun, J., Fortino, G., Guerrieri, A., Jung, J. (eds) Internet and Distributed Computing Systems. IDCS 2018. Lecture Notes in Computer Science(), vol 11226. Springer, Cham. https://doi.org/10.1007/978-3-030-02738-4_16
Download citation
DOI: https://doi.org/10.1007/978-3-030-02738-4_16
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-030-02737-7
Online ISBN: 978-3-030-02738-4
eBook Packages: Computer ScienceComputer Science (R0)