Boost of online social networks has demonstrated that some people are willing to share their views about everyday problems, including noise. With the advent of Natural Language Processing and Machine Learning technologies to the majority of the scientific fields, we have begun to analyze the textual content of social media, and more specifically online social networks, to extract insights about the noise attitude of the population that uses this channel to express their opinion in this matter. Some of the state-of-the-art algorithms, such as deep neural networks, are supervised machine learning algorithms. This means that researchers have to provide a set of labelled training data to build new models. The annotation process is known as one of the most time-costly tasks in a data science pipeline, since researchers among other thigs have to test the agreement between annotators and to measure the quality of the categories they had previously defined. For that reason in this paper, we introduce Noytext which is a customizable web tool to annotate texts from your database, that can be deployed in your own webserver and you can use to request help from colleagues and collaborators in the annotation process in a friendly way.