Noytext: A Web platform to annotate social media documents
on noise perception for their use in opinion mining research.
Universidad Politécnica de Madrid (Spain) - EIT Digital Doctoral School Madrid
Asensio, César 2; De Arcas, Guillermo3
Universidad Politécnica de Madrid (Spain)
Télécom ParisTech (France)
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
Keywords: Text mining, Community engagement, Noise annoyance
I-INCE Classiﬁcation of Subject Number:56, 61, 66, 69
The advent of the Digital Revolution has radically changed the way we communicate
and use technology. Today 4200 million people have an Internet connection, there are
more than 3000 million users who actively use social media, and it is estimated that each
user has an average of 5.5 accounts on these social platforms . The importance of these
changes has even led to the "human being" being named person of the year by Times
magazine in year 2006 . This shows the possibilities opened up by the Internet for
users’ opinions to be heard and for them to have decision-making power as a whole on
issues such as politics, commercial products and the environment, among others.
In parallel, innovative techniques have reached all branches of science, including
environmental acoustics. On the one hand, the lower cost of instrumentation has made
it possible to implement aﬀordable monitoring networks, both in combination with
traditional monitoring devices  and sensor networks composed entirely of low-cost
equipment . On the other hand, the ease with which citizens can be involved in
projects has led to the creation of many crowdsourcing platforms that allow population
to collaborate with research in noise monitoring and acoustic evaluation tasks, some of
those projects were focused on measuring noise , evaluating it , or combining both
approaches . Additionally, the proof of good performance of Artiﬁcial Intelligence
systems has led to the development of programming libraries that have democratized the
use of Machine Learning techniques that are being applied in a multitude of analyses
which were unthinkable a few years ago, such as the automatic identiﬁcation of sound
Despite all this progress, there is an entry barrier for the application of these
technologies. It is a fact that we work in an interdisciplinary environment where acoustic
engineers, urban planners, environmentalists, policy makers, and computer scientists
work all together, but it is also true that many teams do not have easy-to-use tools to start
working with novel data-analysis approaches. For that reason, in this paper we present
Noytext, an open source web application that is relatively easy to install, conﬁgure
and personalize with the aim of simplifying one of the most time-consuming tasks in
supervised machine learning projects: the data annotation.
2. THE PROBLEM
Currently, people that are active on Online Social Networks (OSN) observe, analyze,
create and disseminate information about their feelings and opinions by writing texts, and
uploading photographs and videos. In environmental acoustics, OSN data was used for
the ﬁrst time to diagnose New York noise sources by doing the analysis together with open
data [9, 10].Yahoo researchers carried out the Chatty Maps project, where they analyzed
tags from pictures and they were able to extract the primary noise source in big cities
around the world at street level just using social media content . More recently, we
developed a Machine Learning model to detect and classify noise complaints written on
Twitter . During this project, we had to deal with the lack of annotated datasets and the
diﬃculty to ﬁnd environmental acoustic professionals to help us with that task. It is known
that data annotation is one of the most time-consuming tasks in the data analysis pipeline;
hence, we developed a web application to ask for assistance from other professionals who
wanted to collaborate by annotating some text documents. After obtaining successful
results in the project, in which we demonstrated that it was possible to detect the noise
complaints that people share on OSN, we decided to improve the tool and make it open
source so that other researchers who wanted to apply a similar methodology could do
their research more eﬃciently.
Noytext was created with the aim to help environmental acoustic researchers to
annotate text documents for acoustic perception opinion mining . It oﬀers several
features to be suitable to the needs of every research project in this ﬁeld, including the
possibility of sharing the application as a web page to request annotations from the
Figure 1: Noytext logotype
This web application oﬀers some features that can allow researchers to obtain better
training data, such as:
–Simplifying the annotation task: Since the annotating process is interactive, the user
can tag a greater number of texts than with other plain text methods. In addition,
since annotation is carried out interactively on a web server, rules can be set to
maximize the performance of this task by displaying new texts when they reach a
speciﬁc number of annotations.
–Easy to install in your own server: Noytext is a Shiny application that can be easily
installed and used in a server with a Shiny server installed or a personal computer
with an R installation. If you decide to install it on an empty server on the cloud,
you could have your app working in 20 minutes following the steps given on the
–Standard back-end and front-end: The app is written on R programming language,
code, the standard front-end technologies on web development. The server side,
also known as back-end, works using R language, a standard programming
language in data science and research that will allow researchers to modify code
according to their needs if required. On the other hand, MongoDB is the database
system chosen for the app because of its easy adaptability to web-apps causality.
–Cross platform: In order to facilitate the annotation tasks to users, we have
developed an application whose User Interface (UI) adapts to the device from
which the annotation is carried out.
3.1. Structure and customizing options
Noytext is based on a 4-page schema that you can fully personalize based on your
–Project information page: You can use your own HTML page to provide
information about your project goals, or modify the example HTML ﬁle we
provide you to better match the app appearance. This page can be disabled if you
do not need it.
–Help page: This page gives the user a step-by-step guide on how to use the UI
for data annotation. You can change the hints to your own language or needs, and
disable the page as well.
–Annotation page: This is the main page of the app. It is composed by three main
elements. The text box, where it will appear the texts store in your database; the
radio button selectors, where the annotations categories are shown; and the buttons,
which are used to save the selected option and to show another text. In order to
save and next buttons until a category is selected, making it diﬃcult for potential
fake annotators to boycott the database labeling.
–About page: This is another HTML page that you can use to present information
about your team, institution or whatever you consider. In the same way as in the
information page, you can use your own HTML ﬁle, modify the example or disable
An example of the application’s UI can be seen on Figure 2. Both the page titles
and the project name can be deﬁned by the researcher, being the Noytext logo the only
element that is ﬁxed by default. At the time of writing this manuscript, Noytext does not
Figure 2: Graphic interface of the Noytext platform. On the left, the UI adapted for a
large screen can be seen. On the right, the UI is shown adapting to small screen such as
allow changing the categories of text classiﬁcation, but it will be a customizable element
in future versions of the app. It can also be seen that the UI elements adapt to the width of
the device, hence it can be used both in standard personal computers as well as in tablets
and smartphones. On the other hand, the ﬁgure also shows an example of the type of texts
that can be annotated on the platform: The screenshot on the left shows a typical noise
complaint that can be found on a social network like Twitter and that must be tagged
before training a complaint detection model; the one on the right shows the typical tweet
that mentions the word noise, but is just a post in which the user has shared a piece of
news about noise instead of a complaint.
3.1.1. Decide your own application functionality
The application oﬀers several options to personalize its functionality:
– A database running in a remote MongoDB server can be conﬁgured to be used
with the application. This gives versatility and the option of using Noytext without
changing your computer systems conﬁguration.
– The number of times the same text can be annotated by diﬀerent users can be
deﬁned by the researcher. This allows to get inter-annotator agreement statistics,
used to measure consensus among them, and to check that annotation tasks are
– When the project requires it, information about each annotator could be obtained.
If this option is enabled, a login and registration button will appear. Then, the
ﬁrst time a user enters to Noytext, he will be asked to register and answer a small
questionnaire deﬁned by the researcher. This questionnaire has several types
of survey questions, both free-text and numeric inputs, sliders, radio buttons,
or multiple choice questions, which have been implemented in order to provide
ﬂexibility for this purpose. The variety of inputs can be seen in Figure 3.
Figure 3: Questions types that can be used on Noytext questionnaires
As we have previously mentioned, one of the problems we experienced during our
project to detect and classify noise complaints from OSN was the lack of annotated
datasets. In fact, this was the most time-consuming task of the project, standing for
approximately 30% of the whole duration.
For that reason, and with the aim of testing the application functionality, we launched
the experiment Noise tweet lab. In this experiment we seek collaboration from both
environmental acoustics professionals and the general public to build a reliable labelled
database that can be used by other researchers in the ﬁeld. The creation of this database
will allow researchers to focus on improving noise complaint detection algorithms and
on the eﬃciency of the research projects of this nature by minimizing the data annotation
process. If you want to know more information about this project, you can scan Figure 4
code or access to http://noisetweetlab.noytext.com
Figure 4: Access the website of the Noise Tweet Lab experiment built using Noytext app
As it has been introduced in this manuscript, text annotation is one of the problems
that researchers who decide to apply machine learning techniques to detect textual noise
complaints may face. For this reason, Noytext has been developed and presented. This
open-source application allow you to obtain better training data by simplifying the
annotation task to your annotators. It has shown several customization options, as well
as the Noise Tweet Lab experiment, conceived to create the ﬁrst public annotated dataset
of noise complaints gathered from Social Media, that will be useful for future research
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