Brazil

Identifying subjective sentences and classifying the polarity of subjective sentences are two important tasks in sentiment analysis. Besides being a hot topic, there is still a lack of resources to perform the mentioned sentiment analysis tasks in the Portuguese language, with its syntactic specificities. This paper describes the identified challenges and next steps in an initial study regarding the classification of subjectivity and polarity of sentences with a small set of syntactic features extracted directly from the text. Our approach reached satisfying results in experiments with two classic machine learning models in four datasets consisting of user reviews from different domains.

WebMedia 2020

Investigating sets of linguistic features for two sentiment analysis tasks in Brazilian Portuguese web reviews

web reviews

sentiment analysis

natural language processing

The 9th Brazilian Conference on Intelligent Systems (BRACIS), the 16h Encontro Nacional de Inteligência Artificial e Computacional (ENIAC), and the 12th Concurso de Teses e Dissertações em Inteligência Artificial e Computacional (CTDIAC) was held from 20 to 23 October, 2020.

BRACIS originated from the combination of the two most important scientific events in Brazil in Artificial Intelligence (AI) and Computational Intelligence (CI): the Brazilian Symposium on Artificial Intelligence - SBIA (22 editions), and the Brazilian Symposium on Neural Networks - SBRN (13 editions). ENIAC is a national forum for researchers, practitioners, educators and students to present and discuss innovations, trends, experiences, developments and work in progress in the fields of Artificial and Computational Intelligence. CTDIAC is a satellite event whose goals are to disseminate and award the best dissertations and theses (MSc and PhDs concluded in Brazilian universities from June 2018 to May 2020) in Artificial and Computational Intelligence areas.

In this year, the Symposium on Knowledge Discovery, Mining and Learning (KDMiLe) will be held in conjunction with BRACIS 2020. This sysposium aims at integrating researchers, practitioners, developers, students and users to present their research results, to discuss ideas, and to exchange techniques, tools, and practical experiences – related to the Data Mining and Machine Learning areas.

BRACIS 2020

technical paper

Promoted by the Brazilian Computer Society (SBC), the Brazilian Symposium on Multimedia and Web (WebMedia) is the main event of the theme in Brazil and an excellent opportunity for scientific and technical exchange among students, researchers and professionals in the areas of Multimedia, Hypermedia and Web. This year we will be celebrating to 26th edition of WebMedia, which will be held online from november 30th to December 4th .

Webmedia 2020’s programming includes:

– Technical sessions for the presentation of scientific papers
– National and international keynotes
– Panel with companies
– Workshops
– Short Courses
– Open community meetings

The topics of interest for submitting papers are:

– Web and Social Networks
– Multimedia
– Digital TV, Ubiquitous and Mobile Computing

Besides the main track, WebMedia 2020 will have in parallel the Tools and Applications Workshop, works Tools and Applications Workshop, the Workshop on Undergraduate Research Work and the Workshop The Future of Interactive.

The WebMedia 2020 organization is under the responsibility of the Programa de Pós-graduação Doutorado em Ciência da Computação – Associação UFMA/UFPI (DCCMAPI), do Programa de Pós-graduação em Ciência da Computação/ CCET (PPGCC), do Laboratory of Advanced Web Systems/UFMA (LAWS) e do Laboratório de Sistemas Multimídia/UFMA (TELEMÍDIA).).

#PartiuWebMedia2020 #WebMedia2020

Promoted by the Brazilian Computer Society (SBC), the Brazilian Symposium on Multimedia and Web (WebMedia) is the main event of the theme in Brazil and an excellent opportunity for scientific and technical exchange among students, researchers and professionals in the areas of Multimedia, Hypermedia and Web.

People with visual impairments suffer from the incapacity to un-derstand contextual information in videos, such as the place wherecharacters are, or any other non-spoken actions in general. Somecontent creators address this issue by providing a secondary audioto describe such information, calledAudio Descriptions(ADs). How-ever, some works in the literature have highlighted that people withvisual impairment are usually not able to completely understandscene changes based only on characters’ voices or traditional ADs.Moreover, traditional ADs do not completely describe some of theimportant visual information, such as the background scenery (e.g.colors, furniture) and characters’ details (e.g. blond woman using ared dress). In this work, we propose incrementing the traditionalAD techniques with the usage of interactive video features presentin TV systems. More precisely, the proposed interactivity enablesusers to access specialized AD for different visual information (e.g.,scene, scenario, character). To support the development of such in-teractive content, we present an application template, which helpsto create the final interactive-enhanced video application. As aproof of concept for our approach, we created an interactive ADfor an independent video mainly composed of visual information,with only a few talks.

ListeningTV: Accessible Video Using Interactive Audio Descriptions

The Web continues to grow day after day, being the largest source of data in several domains of knowledge. Such a data are particularly available into billions of HTML pages. Due to it, Web scraping is an increasing activity with a great focus on structured data, like Web tables and Web lists. While the literature about extraction and matching of Web tables is extensive, this is not the same for Web lists. The main reason for that is the absence of a heading with fixed and explicit attributes into a Web list, which makes difficult to find out similar data for matching purposes. Related work about Web lists similarity do not deal with the comparison of the content of two different lists. In order to fill this gap, this paper proposes a similarity function for HTML-coded lists called SimList. It innovates by comparing not only the content of the two input lists but also data on the HTML pages that surround the lists and may contribute to infer their contexts. Besides, SimList may be customized by the user in terms of weights for the internal and surrounding data, as well as auxiliary text similarity measures. Preliminary experimental evaluations show that our similarity function had obtained good precision, recall and f-measure scores even considering the inherent heterogeneity of HTML lists on the Web.

A Similarity Function for HTML Lists

Big companies usually have human and financial resources to personalize their websites. On the other hand, small and medium-sized companies usually do not have such resources. In this paper we propose ALARM: A Light Application for Recommendation and Monitoring. This free platform enables automatic recommendations and monitoring in small and medium-sized websites. The platform is independent of the site structure, as well as monitoring and recommendation methods which may be used in it. We illustrate the features of the platform in a case study, where we show how it can be used to provide recommendations as well as to analyze them.

ALARM: A Light Application for Recommendation and Monitoring

Fake news has been around for a long time. But with the advancement of social media and internet access, fake news has become a bigger problem. Because of the rapid spread in social media and instant messaging applications, fake news can reach more people in less time by directly influencing democratic processes, leveraging security issues that sometimes lead to tragic ends. In order to promote a fast and automated method of fake news identification, in this study, we performed an analysis of false Brazilian news, identifying writing patterns through natural language processing and machine learning.

Mineração de dados aplicada à identificação de notícias falsas

In 2020, COVID-19 pandemic is one of the most talked-about subjects on social networks. This subject has generated discussions of great importance about politics, economics, medical advances, people’s awareness, preventive techniques, etc. Using sentiment analysis and topic modeling techniques, in this paper, we aim to present an analysis of the messages from the social network Twitter during the pandemic of COVID-19. For this, we use a tweets dataset to train a sentiment classifier and then use the NMF algorithm to perform the interest topic generation.


Relacionando Modelagem de Tópicos e Classificação de Sentimentos para Análise de Mensagens do Twitter Durante a Pandemia da COVID-19

Decretos administrativos são emitidos pelo Poder Executivo com o objetivo de controlar despesas, agências, ministérios e planos do governo. Apesar de serem textos de importância estratégica, no Brasil não existem classificações agregadas que explicitam as áreas de influência de cada um desses documentos. Sendo assim, neste trabalho apresentamos duas metodologias de classificação e sua automatização através de algoritmos clássicos e do estado da arte em aprendizado de máquina para modelagem textual. Nossos resultados explicitam desempenho de cerca de 80% de F1-score para alguns desses modelos, além de apresentarem uma visão geral desse tipo de dado e suas possibilidades e culminarem com a ampliação da transparência das ações e decisões do Poder Executivo Federal.

Processamento de linguagem natural aplicado à classificação de decretos administrativos brasileiros

Video annotation is an activity that aims to supplement this type of multimedia object with additional content or information about its context, nature, content, quality and other aspects. These annotations are the basis for building a variety of multimedia applications for various purposes ranging from entertainment to security. There are automatic methods for video annotation. However, these methods require specific conditions and features that are not even found in actual application scenarios. Manual annotation is a strategy that uses the intelligence and workforce of people in the annotation process and is an alternative to cases where automatic methods cannot be applied. However, manual video annotation can be a costly process because as the content to be annotated increases, so does the workload for annotating. Crowdsourcing appears as a viable solution strategy in this context because it relies on outsourcing the tasks to a multitude of workers, who perform specific parts of the work in a distributed way. However, as the complexity of required media annoyances increases, it becomes necessary to employ skilled labor, or willing to perform larger, more complicated, and more time-consuming tasks. This makes it challenging to use crowdsourcing, as experts demand higher pay, and recruiting tends to be a difficult activity. In order to overcome this problem, strategies based on the decomposition of the main problem into a set of simpler subtasks suitable for crowdsourcing processes have emerged. These smaller tasks are organized in a workflow so that the execution process can be formalized and controlled. In the literature, there are some different proposals for the construction of this type of workflow, but each of them presents limitations that still have to be overcome. In this sense, this thesis aims to present a new framework that allows the use of crowdsourcing to create applications that require complex video annotation tasks. The developed framework considers the whole process from the definition of the problem and the decomposition of the tasks, until the construction, execution, and management of the workflow. This framework, called CrowdWaterfall, contemplates the strengths of current proposals, incorporating new concepts, techniques, and resources to overcome some of its limitations.

Integrating Crowdsourcing and Human Computation forComplex Video Annotation Tasks

One of the primary ways to expand a business or to keep it stable during a crisis is to create partnerships with other companies. With that, this study presents results regarding a new data model, which explores user reactions on social media to indicate strategic business partnerships. There are three main contributions of this study to the literature: (i) a business relationship network model; (ii) a business community detection algorithm; and (iii) a business outlier detection algorithm. The evaluation of the contributions was performed exploring real data of approximately 280 million user reactions on Facebook. Results suggest that business partnership recommendation is possible using the information available in social media.

Business Relationship Network Model from Social Reactions Data

As the facial recognition research field grows, so do the possibilities for digital TV applications. However, in the current state of the art, it is not safe to assume there is a certain algorithm that would be the best for all kinds of applications. This work proposes an architecture to enable facial expression recognition for TV in a way that is agnostic to the recognition algorithm. As proof of concept, the proposal was developed for the Ginga middleware. There are two implementations: the first one, based on the current version of the Ginga middleware, and the second one, based on a proposed extended version of the middleware, exploring the viability of the present work.

Possibilitando o Reconhecimento de Expressões Faciais em Aplicações Ginga-NCL

This paper presents the prototype of a compact and low-cost we- arable electronic device that, based on the reading of the Wi-Fi signal strength emitted by other wearable devices of the same type, estimates the proximity between users and issues a notification (an audible and visual alarm) when the distance between them is less than a reference value. Our preliminary results suggest that the proposed solution may be a viable alternative to ensure social distancing as a practice to face the pandemic caused by COVID-19.

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ListeningTV: Accessible Video Using Interactive Audio Descriptions

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Investigating Sentences Features for Subjectivity and Polarity Classification in Brazilian Portuguese

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