Although very useful, aspect-based sentiment analysis shows several challenges. The occurrence of implicit aspects is one of them. To improve our understanding of implicit aspects, this work analyzes their composition and categorizes the implicit aspect clues found in two corpora of opinionated texts of different domains in the Portuguese language. As results of this work, in addition to the implicit aspect typology, we present four lexicons of implicit aspect clues for different domains and the annotation of implicit aspect clues in an existing corpus.

Implicit opinion aspect clues in Portuguese texts: analysis and categorization

This sentiment analysis work is focused on the task of identifying aspects, emphasizing the so-called implicit aspects, i.e., those that are not explicitly mentioned in the texts. For this, we analyzed frequency-based methods, adapted rules from the English language to Portuguese, and developed a method that learns new rules through corpus analysis.

Learning rules for automatic identification of implicit aspects in Portuguese

In this research, qualitative and quantitative characteristics of literary genres of the BDCamões corpus are evaluated under the bottom-up perspective. We show that textual genres make information more easily understood by search tools. We started from chronicles, novels, short stories, and tales annotated according to the technique of universal dependence classified according to random decision forests. The use of the LIWC dictionary in Brazilian-Portuguese gave access to psycholinguistic resources. The results are presented in two tables: one with the results by class reported by the mean together with a measure of variability; and another with the importance of characteristics by class, LIWC labels, part of speech and universal dependency labels, highlighting the ten highest positive characteristics and the ten lowest negative characteristics. These tools have adapted to the contextual characteristics of fluidity and changeability of literary genres, overcoming the difficulty of standard NLP varieties These results have potential uses to replicate contextual characteristics, contemplating the artistic content of texts, which makes the machine more intuitive and with more consistent results.

A machine learning approach to literary genre classification on Portuguese texts: circumventing NLP's standard varieties

Text simplification (TS) is a natural language transformation process that reduces linguistic complexity while preserving semantics and retaining its original meaning. This work aims to present a research proposal for automatic simplification of texts, precisely a split-and-rephrase approach based on an encoder-decoder neural network model. The proposed method was trained against the WikiSplit English corpus with the help of a part-of-speech tagger and obtained a BLEU score of 74.72%. We also experimented with this trained model to split-and-rephrase sentences written in Portuguese with relative success, showing the method’s potential.

Experimenting Sentence Split-and-Rephrase Using Part-of-Speech Labels

Machine translation is intrinsically associated with the study and development of computerized methodologies for idiomatic translations' production. The most common approaches are the statistical and methods based on neural networks. One of the deficiencies pointed out by these methods is the possible lack of coherence between the translated sentences. In this project, we propose using techniques based on Graph Theory to preserve the coherence in the translation of texts from English to Portuguese. The studied method presents large performance variability; however, some translations produce sentences 90% better evaluated than the statistical translator Moses and 10% superior to Google Translate.

Post-processing of machine translation texts based on graph theory

This study presents an algorithmic strategy to analyze a small set of social network information to monitor the dengue disease. Previous studies have achieved similar results based on large datasets of Twitter microblogs. In this study, we successfully map dengue cases using a small data collection of tweets from a medium-size city. A set of modules were constructed to collect, categorize, and display dengue-related tweets. We compared the collected tweets with real data from confirmed dengue cases. We showed a significant correlation between the number of confirmed dengue cases and the number of dengue-related tweets, even considering such a small dataset. The results of this approach may be relevant in public health policies.

Statistical analysis of small twitter data collection to identify dengue outbreaks

Please click on the **[SEE ALL](https://underline.io/events/285/posters?eventSessionId=10354)** button on the right side to view all posters.

Poster Session 1

poster

The International Conference on Computational Processing of Portuguese (PROPOR) is the main event in the area of human language processing that is focused on theoretical and technological issues of written and spoken Portuguese and Galician. The meeting has been a very rich forum for the exchange of ideas and partnerships for the research and industry communities dedicated to the automated processing of Portuguese, promoting the development of methodologies, resources and projects that can be shared among researchers and practitioners in the field.

Find more information and pay the registration fee on [event organizer’s website](https://sites.universidadedefortaleza.com/propor2022/?page_id=401)

PROPOR 2022

The International Conference on Computational Processing of Portuguese (PROPOR) is the main event in the area of human language processing that is focused on theoretical and technological issues of written and spoken Portuguese and Galician.

**Chairs:** Tiago Torrent and Evandro Ruiz




STIL Main Conference Session B: Opinion Mining and Sentiment Analysis

technical paper

**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. **STIL** is the bi-annual Language Technology event.
All events are supported by the Brazilian Computer Society – Special Interest Groups: **CE-IA**, **CE-IC** and **CE-PLN**.

To access the 10th Brazilian Conference on Intelligent Systems (**BRACIS**), you need to register for the conference.  Please visit the Conference official site to register

[Register now](http://c4ai.inova.usp.br/bracis2021/)

Register now

BRACIS 2021

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. 
STIL is the bi-annual Language Technology event.
All events are supported by the Brazilian Computer Society – Special Interest Groups: CE-IA, CE-IC and CE-PLN

 **Chairs:** Oto Araújo Vale,	Evandro Ruiz and Tiago Torrent



STIL Main Conference Session F: Evaluation

KDMiLe: Technical Session 2: Machine Learning and 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.

Evandro Ruiz

6

SHORT BIO

Presentations

Implicit opinion aspect clues in Portuguese texts: analysis and categorization

Learning rules for automatic identification of implicit aspects in Portuguese

A machine learning approach to literary genre classification on Portuguese texts: circumventing NLP's standard varieties

Experimenting Sentence Split-and-Rephrase Using Part-of-Speech Labels

Post-processing of machine translation texts based on graph theory

Statistical analysis of small twitter data collection to identify dengue outbreaks

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