Adaptive contents for interactive TV guided by machine learning based on predictive sentiment analysis of data
Mondragón, Victor M
González-Crespo, Rubén (UNIR)
Tipo de Ítem:Articulo Revista Indexada
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.
Este ítem aparece en la(s) siguiente(s) colección(es)
Mostrando ítems relacionados por Título, autor o materia.
Supporting academic decision making at higher educational institutions using machine learning-based algorithms Nieto, Yuri; García-Díaz, Vicente; Montenegro, Carlos Enrique; González-Crespo, Rubén (UNIR) (Soft Computing, 2018)Decisions made by deans and university managers greatly impact the entire academic community as well as society as a whole. In this paper, we present survey results on which academic decisions they concern and the variables ...
An Intelligent Mobile Web Browser to Adapt the Mobile Web as a Function of the Physical Environment Pascual-Espada, Jórdan (UNIR); García-Díaz, Vicente; González-Crespo, Rubén (UNIR); Pelayo García-Bustelo, B Cristina; Cueva Lovelle, Juan Manuel (IEEE Latin America Transactions, 11/2013)Today, millions of users all over the world visit websites using their Smartphones. Mobile devices have several features that may contribute to worsen the user experience when using mobile webs, such as: small displays and ...
Fuzzy system to adapt web voice interfaces dynamically in a vehicle sensor tracking application definition Cueva-Fernandez, Guillermo; Pascual-Espada, Jórdan; García-Díaz, Vicente; González-Crespo, Rubén (UNIR); García-Fernandez, Nestor (Soft Computing, 08/2016)The Vitruvius platform is focused on vehicles and the possibility of working with their multiple sensors, and the real-time data they can provide. With Vitruvius, users can create software applications specialized for the ...