Open Access Journal

ISSN : 2394-2320 (Online)

International Journal of Engineering Research in Computer Science and Engineering (IJERCSE)

Monthly Journal for Computer Science and Engineering

Open Access Journal

International Journal of Engineering Research in Computer Science and Engineering (IJERCSE)

Monthly Journal for Computer Science and Engineering

ISSN : 2394-2320 (Online)

To Evaluate & Predict the Television Serials‟ TRP

Author : Amritesh Sidhu 1 Er. Kanwal Preet S. Attwal 2 Dr. Gaurav Gupta 3

Date of Publication :11th September 2019

Abstract: In present scenario, the number of TV reality shows are growing day-by-day. Television rating points (TRPs) play a vital role in the broadcasting field of TV. TRPs can also be deployed to study the interests of the viewers. In this paper, firstly viewers’ opinions are examined. Then six factors have been considered based on which, a random tree is made. This random tree is a part of classification (a technique of data mining). Finally, this random tree helps to predict the TRP of a serial or a channel.

Reference :

    1. A.Bria, P. Karrberg, P. Andersson(2007), “TV in the mobile or TV for the mobile: challenges and changing value chains”, IEEE International Symposium on Personal, Indoor and Mobile Radio Communications, pp. 1-5.
    2. A. Mhaisgawali, N. Giri (2014), “Detailed descriptive and predictive analytics with twitter based TV ratings”, International Journal of Computing and Technology, 1(4), pp. 125-130.
    3. A. Rashid, N. Anwer, M. Iqbal, M. Sher (2013), “A survey paper: areas, techniques and challenges of opinion mining”, International Journal of Computer Science Issues, 10(2), pp. 18-31.
    4. A. Singh, S. K. Mehta, H. G. Mishra (2011),“TRP as a measure of visual communication: a study of Jammu city, India”, International Conference on Economics and Finance Research, 2(7), pp. 123-137
    5. B. M. Ramageri (2014), “Data mining techniques and applications”, Indian Journal of Computer Science and Engineering, 1(4), pp. 301-305.
    6. D. Anand, A. V. Satyavani, B. Raveena, M. Poojitha (2018), “Analysis and prediction of television show popularity rating using incremental k-means algorithm”, International Journal of Mechanical Engineering and Technology, 9(1), pp. 482-489.
    7. E. Panova, A. Raikov O.Smirnova(2015), “Cognitive television viewer rating”, International Young Scientists Conference on Computational Science, 66, pp. 328–335.
    8. G. Vinodhini, R. M. Chandrasekaran (2012), “Sentiment analysis and opinion mining: a survey”, International Journal of Advanced Research in Computer Science and Software Engineering, 2(6), pp. 283-292.
    9. L. Molteni, J. P. D. Leon (2016), “Forecasting with twitter data: an application to USA TV series audience”, International Journal of Design and Nature and Ecodynamics, 11(3), pp. 220-229.
    10. M. J. Panaggio, P. W. Fok, G. S. Bhatt, S. Burhoe, M.Capps, C. J. Edholm, F. E. Moustaid, T. Emerson, S. L. Estock, N. Gold, R. Halabi, M. Houser, P. R. Kramer, H. W. Lee, Q. Li, W. Li, D. Lu, Y. Wian, L. F. Rossi, D. Shutt, V. C. Yang, Y. Zhou (2016), “Prediction and optimal scheduling of advertisements in linear television”, Mathematics Department, RoseHulman Institute of Technology, pp. 2-24.
    11. N. Rikhi (2015), “Data mining and knowledge discovery in database”, International Journal of Engineering and Trends Technology, 23(2), pp. 64- 70.
    12. P. G. Malur, N.Lakshmikantha, V.Prashanth(2014),“Reeling the reality: A study on contemporary reality shows and their Influence on other entertainment program genres”, International Research Journal of Social Sciences,3(8), pp. 35-38.
    13. P. Jain, P. Jakate, A. Dhotre, J.Bhati (2015), “A novel approach to analysis of TV shows using social media, machine learning and big data”, International Journal of Technological Exploration and Learning, 4(6), pp. 604-612.
    14. R. N. Rao, H. Vani, S. Vandana (2015),“A study of viewers satisfaction towards hindi news channels at Hyderabad”, Indian Journal of Commerce and Management Studies, 6(1), pp. 62-69.
    15. R. Pagano, M. Quadrana, P. Cremonesi, S. Bittanti, S. Formwentin, A. Mosconi (2015), “Prediction of TV ratings with dynamic model”, ACM Workshop on Recommendation System for Television and Online Video,
    16. R. Hinami, S. Satoh (2017), “Audience behavior mining: integrating TV ratings with multimedia content”, IEEE Computer Society, 24(2), pp. 44-54.
    17. S. Pankanti, S. Chavan, M. Kumar, P. Vitkar (2017), “A TV show suggestion framework In view of Viewer‟s rating”, International Journal of Engineering Research and Technology, 10(1), pp. 370-373.
    18. S. Patil, Y. Jog, S. Pareek, D. Chodnekar (2015), “Challenges and opportunities of television rating point (TRP) and television audience measurement (TAM) in india”, International Journal of Advanced Technology in Engineering and Science, 3(10), pp. 23-34.
    19. S. Sereday, J. Cui (2017),“Using machine learning to predict future TV ratings”, Nielsen Journal of Measurement, 1(3), pp. 3-12.
    20. T. Kadam, G. Saraf, V. Dewadkar, P. J. Chate (2017), “TV show popularity prediction using sentimental analysis in social network”, International Research Journal of Engineering and Technology, 4(11), pp. 1087-1089.

Recent Article