Abstract – Now a days because of increasing amount of TV programs and users’ mixed preference recommending interesting programs for users is challenging task and opinion mining is quite difficult. In this paper we propose and describe framework for estimating public interest, opinion analysis based on IPTV user behavior and focuses primarily on   channel change events and opinion mining concepts.

Keywords: IPTV (Internet Protocol Television), CCE (Channel Change Event), Interest Score, Opinion mining

[1] M. Kren, A. Kos, Y. Zhang, A. Kos, and U. Sedlar, “Public Interest Analysis Based on Implicit Feedback of IPTV Users,” IEEE Trans. Ind. Inform., vol. 13, no. 4, pp. 2077–2086, IEEE,2017.

[2] M. Kren, U. Sedlar, J. Bešter, and A. Kos ,“Determination of User Opinion Based on IPTV Data”,  ICTON 2016.

[3] JieZhang, KuangDu, Ruihua Cheng, “Reliable Gender Prediction Based on Users Video Viewing Behavior”, 2016 IEEE 16th International Conference on Data Mining, IEEE 2016.

[4] YueTeng, LiangHe “Rating Prediction Algorithm and Recommendation Based on User Behavior In IPTV”, IEEE,2012.

[5] Y. Hu, Y. Koren, and C. Volinsky, “Collaborative filtering for implicit feedback datasets,” 8th International Conference on Data Mining (ICDM), IEEE, 2008.

[6]G. Dziczkowski, K. Wegrzyn-Wolska, and L. Bougueroua,“An opinion mining approach for web user identification and clients' behaviour analysis,” 5th International Conference on Computational Aspects of Social Networks (CASoN), 2013.

[7] Fulian Yin, Lu Lu, “Crowd Mining System for TV Program Based on Audience Behavior Analysis”, 2015 International Conference on Cyber-Enabled Distributed Computing and Knowledge Discovery, IEEE, 2015.

[8] U. Sedlar, M. Volk, J. Sterle, A. Kos, and R. Sernec, “Contextualized monitoring and root cause discovery in IPTV systems using data visualization,” IEEE Network, vol. 26, no. 6, pp. 40–46,IEEE, 2012.

[9] Zheng Chen, Jinyao Yan, Fan Zhang “Feature Extraction Algorithm and Optimizations for Mass TV Audience”, 7th International Conference on Advanced Computational Intelligence Mount Wuyi, Fujian, China; March 27-29, 2015,IEEE  2015.

[10] M. Kren, A. Kos, and U. Sedlar, “Mining the IPTV channel change event stream to discover insight and detect ads,” Mathematical Problems in Engineering, 2016.

[11] Yun Gaoy, XinWeiy, “Mining IPTV User Behaviors with an Enhanced LDA Model”, 2018 IEEE.

[12] Sang-Yun Lee, Jeong-Woo Son, “Audience’s Viewing Behavior Analysis for Inferencing Consumer Preferences”, ICTC 2015, IEEE, 2015.

[13] Naors Y Anad Alsaleem1, Riyad Mubarak Abdullah “Mathematical Models of Peer to Peer Networks for Stream IPTV Transmission”, Naors Y AnadAlsaleem, The 9th IEEE International Conference on Dependable Systems, Services and Technologies, IEEE 2018.

[14] V. M. M. Maca, V. G. Diaz, J. P. Espada, and V. B. Semwal, “Measurement of viewer sentiment to improve the quality of television and interactive content using adaptive content,” in Electrical, Electronics, and Optimization Techniques (ICEEOT), International Conference on, IEEE,2016, pp. 4445–4450.

[15] Mo.chen, ZieJhang,“An implicit feedback integrated LDA-based topic model for IPTV program Recommendation”,IEEE,2016.

[16] MinhaeKwon, Jungmin Kwon, “An Architecture of IPTV Networks Based on Network Coding”, ICUFN 2017,  IEEE, 2017.

[17] Maria Kihl, IñigoSedano, “Application of full-reference video quality metrics in IPTV”, IEEE.

[18] Wen-Chang Tsai, Chih-Lung, “A Lightweight Personalized Image Preloading Method for IPTV System”, ICACT2017, IEEE, 2017.

[19] Jiyoung Lim, InshilDoh, “Authentication for Mobile Open IPTV service Security”, 2016 10th International Conference on Innovative Mobile and Internet Services in Ubiquitous Computing,IEEE,2016

[20] Yang Xiao et al., “Internet protocol television (IPTV): The killer application for the next-generation internet,” Institute of Electrical and Electronics Engineers, IEEE, 2007.