Nowadays there are different projects based on people’s sentiment and reactions analysis in social networks. On the one hand in this kind of project, that includes social network analysis, we can see how the information gathered is represented via graphs in order to obtain conclusions about the information. On the other hand, there are many different projects based on intelligent lighting, the target of this kind of projects is to try to obtain a way to automate the lighting based on the interaction with people. All of this brings us to the representation of sentiments and opinions given in twitter messages via lighting, generating associated colors to the sentiments gathered in social networks and using it to light a room. So that the messages gathered will be analysed and represented in a new and dynamic way. Applied to the artificial intelligence field this TFM’s objective is to develop a case of study that researches ways to represent sentiments via lights that change colors, making these lights change depending on the sentiment captured via the analisys of messages that the users post in a twitter’s hashtag. In order to do this a hardware and software system is going to be developed covering the following specific targets of developing a hardwares controllers that acts over color lights or leds, developing tag clouds builded with topics analysed with LDA and OLDA, developing a software application that is able to read and analyse twitter messages, configuration of a task automation platform in order to give IoT functionality to the system. About the software development, an application that reads and analyses messages will be implemented to gather the sentiments that it contains. This sentiment reader and processing system will be done in Python language and the text analyzer that will be used is senpy which is a cognitive tool developed by the GSI (UPM) laboratory. Regarding the task platform, its intended to include it to get more interaction between the lighting system and other actuators. To cover this it is used EweTasker tool developed by the GSI laboratory. This task platform will be able to manage these parameters to modify the lights and screens. Finally Elasticsearch will be used for storing the results, this storage will be done following the W3C specification using the RDF data model.