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Soft Computing

, Volume 22, Issue 8, pp 2731–2752 | Cite as

Adaptive contents for interactive TV guided by machine learning based on predictive sentiment analysis of data

  • Victor M. Mondragon
  • Vicente García-Díaz
  • Carlos Porcel
  • Rubén González Crespo
Methodologies and Application

Abstract

This paper describes a new proposal for interactive television which is an answer to a continuous change in the traditional television as consequence of the inclusion and evolution of the digital social networks, the Internet and the different elements of the digital age. The digital evolution has encourage the interaction of the viewers with the content and also increases the need to evolved the content, the methods, formats, tools and architectures to adapt the content to the sentiment expressed by the viewer while watching a show. The present paper contains the following objectives: The first objective is to create guidelines that can be used to construct adaptive contents for television, which can be modified in real time by the production team or the director of the show. The second objective is to develop applications that allows to obtain, collect and analyze the sentiment inside of the expressions, data or opinions of the viewers, who interact with the show through social networks or communication channels as: Facebook, Twitter, Instagram and WhatsApp. The third objective is to develop a machine learning to predict the preferences of the viewers, generating options and changes in the sequence of the scenes of the TV show that will be broadcasted in real time. All the objectives explained above are applied to two TV shows which are different in the content but share the live condition. During the broadcasting of the show, the guidelines are applied, the results are obtained, analyzed and the final result is more participation of the viewers and a better perception of the content. As a result of the research and the application in real life of the proposal, this paper contributes with an alternative solution for interactive TV where a viewer can interact with the show and the production team can modify the content according to what the viewers express and expect to watch based on an analysis of sentiment of data using a machine learning.

Keywords

Sentiment analysis Adaptive content Television interactive Machine learning Modeling predictive Real time Plebiscite 

Notes

Acknowledgements

This study has no funding.

Compliance with ethical standards

Conflict of interest

The authors declare that they have no conflict of interest.

Ethical approval

This article does not contain any studies with human participants or animals performed by any of the authors.

References

  1. Alteryx (2016) Alteryx: The Leader in Self-Service Data Analytics, ALteryx, 2016. [En línea]. http://help.alteryx.com/9.5. [Último acceso: 23 03 2016]
  2. Anasse B, Mohamed C, Jung T (2014) Predictive analytics for dummies, p 360Google Scholar
  3. Baldominos Gómez A, Mingueza N, García del Pozo M (2015) OpinAIS: an artificial immune system-based framework for opinion mining. Int J Artif Intell Interact Multimed 3(3):25–34Google Scholar
  4. BBVA (2010) Biplots in practice, BBVA foundation manualsGoogle Scholar
  5. Bernabé-Moreno J, Tejeda-Lorente A, Porcel C, Fujita HH, Herrera-Viedma E (2015) CARESOME: a system to enrich marketing customers acquisition and retention campaigns using social media information. Knowl Based Syst 80:163–179CrossRefGoogle Scholar
  6. Centro Nacional de Memoria Histórica, Rearmados, R, de Desmovilización R (2015) Panorama posacuerdos con las AUC, 1 edn, vol 1. C. N. d. M. Histórica., Ed. Panorama posacuerdos con las AUC, Bogota DC, 2015, pp 513–520Google Scholar
  7. Cesar P, Geerts D (2011) Past, present, and future of social TV, consumer communications and networking conference (CCNC), pp 347– 351Google Scholar
  8. Cesar P, Geerts D (2011) Past, present, and future of social TV. Consumer Commun Netw Conf 49(1):347–351Google Scholar
  9. Collazos Ordóñez CV, Mondragón Pañeda (2008) Lineamientos de usabilidad para el diseño y evaluación de la televisión digital interactiva. Avances en Sistemas e Informática, vol 5, \(\text{n}^{\circ }\). ISSN:19090056, pp 214–218Google Scholar
  10. Del Val E, Martínez C, Botti V (2016) Analyzing users’ activity in online social networks over time through a multi-agent framework. Soft Comput 11(20):4331–4345CrossRefGoogle Scholar
  11. Duong HT, Nguyen DA, Van Huan N, Nguyen VD (2016) Behavior-based video recommendation using adaptive neuro-fuzzy system on social TV. J Intell Fuzzy Syst 1:12Google Scholar
  12. Garrix M (2016) Compositor, lions in the wild. [Grabación de sonido]Google Scholar
  13. Ghoulam A, Barigou F, Belalem G, Meziane F (2015) Using local grammar for entity extraction from clinical reports. Int J Interact Multimed Artif Intell III(3):16–24Google Scholar
  14. González CB, García-Nieto J, Delgado IN, Montes JFA (2016) A fine grain sentiment analysis with semantics in Tweets. Int J Interact Multimed Artif Intell 3(6):22–28Google Scholar
  15. Hu M, Liu yB (2004) Mining and summarizing customer reviews. In: Proceedings of the ACM SIGKDD international conference on knowledge discovery. http://www.cs.uic.edu/~liub/publications/kdd04-revSummary.pdf Original source
  16. Jiang J, Kohler J, Williams C, Zaletelj J, Guntner G, Horstmann H, Weng Y (2011) LIVE: an integrated production and feedback system for intelligent and interactive TV broadcasting. IEEE Trans Broadcast 57(3):646–661CrossRefGoogle Scholar
  17. Julian D (2016) Designing machine learning systems with Python. In: Design efficient machine learning systems that give you more accurate results, 2 edn. Packt Publishing, p 232Google Scholar
  18. Karen C, de Oliveira R (2013) What’s up with whatsapp? Comparing mobile instant messaging behaviors with traditional SMS. ACM, New YorkGoogle Scholar
  19. Kianian S, Khayyambashi M, Movahhedinia N (2016) FuSeO: Fuzzy semantic overlapping community detection. J Intell Fuzzy Syst 1:12Google Scholar
  20. Li WL, Zhang C, Qiu X (2014) Computational intelligence and security (CIS), 2014 Tenth International Conference on identifying relevant messages for social TV vol 53(3), pp 288–292Google Scholar
  21. Lian S (2012) TV content analysis: techniques and applications. Auerbach, 19 March 2012, pp 222–223Google Scholar
  22. Liu B (2011) Web data mining. Springer, BerlinCrossRefzbMATHGoogle Scholar
  23. Login de Telepacifico, Telepacifico, [En línea]. http://www.telepacifico.com/login
  24. Majd E, Balakrishnan V (2017) A trust model for recommender agent systems. Soft Comput 221(2):417–433CrossRefGoogle Scholar
  25. Martinez-Cruz C, Porcela C, Bernabé-Morenob J, Herrera-Viedm yE (2015) A model to represent users trust in recommender systems using ontologies and fuzzy linguistic modeling. Inf Sci 311:102–118CrossRefGoogle Scholar
  26. Mertz D (2013) Text processing in Python, 2 edn. Editorial. Addison-Wesley, pp 55–156. ISBN:0-321-11254-7Google Scholar
  27. Miller TW (2005) The data and text mining: a business applications approach. 9780131400856, Pearson Prentice Hall, Upper Saddle River, p 259Google Scholar
  28. Miller TW (2014) Modeling techniques in predictive analytics: business problems and solutions with R. http://www.ftpress.com/miller, Pearson Education, pp 107–120
  29. Miller TW (2014) Modeling techniques in predictive analytics: business problems and solutions with R. http://www.ftpress.com/miller, Pearson Education, pp 107–120
  30. Moen R, Norman C (2015) Evolution of the PDCA cycle, [En línea]. http://pkpinc.com/files/NA01MoenNormanFullpaper.pdf. [Último acceso: 10 05 2016]
  31. Mondragon Maca VM, Garcia Diaz V, Pascual Espada J, Bhaskar Semwal V (2016) Measurement of viewer sentiment to improve the quality of television and interactive content using adaptive content. International conference on electrical, electronics, and optimization techniques (ICEEOT), vol3(1), pp 143–154Google Scholar
  32. Moreno A, Teófilo R (2016) Text Analytics: the convergence of big data and artificial intelligence. Int J Interact Multimed Artif Intell 3(6):7Google Scholar
  33. Nathan Danneman RH (2014) Social media mining with R. Birmingham, PositionGoogle Scholar
  34. Neira E (2016) Redes Sociales y Televisión: un támden que funciona. de La otra pantalla: redes sociales, móviles y la nueva televisión, EBOOK, Ed., Barcelona, UOC, pp 45–55Google Scholar
  35. Nuñez-Valdez ER, Cueva-Lovelle JM, Sanjuan O, Montenegro-Marin CE, Infante Hernandez G (2011) Social voting techniques: a comparison of the methods used for explicit feedback in recommendation systems. Spec Issue Comput Sci Softw Eng 1(4):61–66Google Scholar
  36. Presidencia de la Republica de Colombia, Acuerdo de Paz, Presidencia de la Republica de Colombia, 01 08 2016. [En línea]. http://www.acuerdodepaz.gov.co/plebiscito. [Último acceso: 04 09 2016]
  37. Ravindran SK, Garg V (2015) Mastering social media mining with R. Mastering social media mining with R: extract valuable data from social media sites and make better business decisions using R. Packt, Birmingham, p 248Google Scholar
  38. Russell MA (2013) Mining the social web,data mining Facebook, Twitter, LinkedIn, Google+, GitHub, and More, 2 edn, vol 2. O’Reilly Media, p 448Google Scholar
  39. Sarkar D (2016) Text analytics with Python: a practical real-world approach to gaining actionable. Apress, Bangalore, p 397Google Scholar
  40. Serrano J, Olivas JA, Romero F, Herrera-Viedma E (2015) Sentiment analysis: a review and comparative analysis of web. Inf Sci 3(1):18–38CrossRefGoogle Scholar
  41. Settouti N, El Amine Bechar M, Amine Chikh M (2016) Statistical comparisons of the top 10 algorithms in data mining for classification task. Int J Interact Multimed Artif Intell 4(1):4Google Scholar
  42. Shih-Hsuan Y, Xiu-Wen L, Ying-Chen L (2016) A design framework for smart TV: case study of the TaipeiTech smart TV system. 2016 IEEE international conference on consumer electronics—Taiwan (ICCE-TW), vol 5(3), pp 241–248Google Scholar
  43. Tejeda-Lorente A, Porcel C, Peis E, Sanz R, Herrera-Viedma E (2014) A quality based recommender system to disseminate information in a university digital library. Inf Sci 261:52–69CrossRefGoogle Scholar
  44. Telepacifico AF (2016) Canal Regional Telepacifico, Telepacifico, [En línea]. http://www.telepacifico.com/afondo/. [Último acceso: 10 8 2016]
  45. Tran VC, Hoang DT, Nguyen NT, Hwang D (2016) A named entity recognition approach for tweet streams using active learning. J Intell Fuzzy Syst 11 (Preprint)Google Scholar
  46. Twitter, Preguntas Frecuentes sobre Retweets (RT). Twitter, 17/3/2016. [En línea]. https://support.twitter.com/articles/230754
  47. Wages R, Grunvogel SM, Zaletelj J, Mac Williams C, Trogemann G (2006) Future live iTV production: challenges and opportunities. Conference automated production of cross media content for multi-channel distribution (AXMEDIS), pp 325–328Google Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2017

Authors and Affiliations

  • Victor M. Mondragon
    • 1
    • 2
  • Vicente García-Díaz
    • 2
  • Carlos Porcel
    • 3
  • Rubén González Crespo
    • 4
  1. 1.National Historical Memory CenterBogotáColombia
  2. 2.Department of Computer ScienceUniversity of OviedoOviedoSpain
  3. 3.Department of Computer ScienceUniversity of JaénJaénSpain
  4. 4.School of EngineeringUniversidad Internacional de La Rioja (UNIR)MadridSpain

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