This thesis is the result of a project whose objective has been to develop and deploy an aspect-based sentiment analyzer applied to restaurant reviews based on Natural Language Processing (NLP) techniques, most of them developed at Intelligent Systems Group. To do so, a system that extracts, contextualizes and classifies aspects was developed. Specifically, each aspect commented by the user in his restaurant review. For testing and evaluating the system we have used a dataset composed by restaurant reviews that allows implementing each module in our analyzer. First, the system realizes the extraction of aspects from each review. With the next module, the context of each aspect is found. Once we have done this, the objective of next modules is to classify in two ways these aspects. First, regarding six possible topics and finally depending on its polarity, that is, if the aspect is valuated positive or negatively Besides, a prototype has been validated with the dataset and a visualization system has been developed using Web Components and D3.js to show analyzer results. As a result, this project allows us to apply aspect-based sentiment analysis tasks to restaurant reviews, which can lead an interesting study of restaurants market and lately applying to other topics.