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Rafael Guimarães Rodrigues

Bio: Rafael Guimarães Rodrigues is an academic researcher from Centro Federal de Educação Tecnológica de Minas Gerais. The author has contributed to research in topics: Brazilian Portuguese & Personality. The author has an hindex of 3, co-authored 9 publications receiving 26 citations. Previous affiliations of Rafael Guimarães Rodrigues include Centro Federal de Educação Tecnológica Celso Suckow da Fonseca.

Papers
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Proceedings ArticleDOI
09 Jul 2019
TL;DR: In this paper, a new Brazilian Portuguese LIWC lexicon (LIWC 2015pt) based on LIWC 2015 program is presented, which outperforms LIWC 2007pt in all three tasks.
Abstract: LIWC is a text analysis program that categorizes words into gram- matical and psychologically derived categories. The currently available LIWC lexicon for Brazilian Portuguese (LIWC 2007pt) is based on the 2007 version of LIWC program. As several studies indicated, LIWC 2007pt shows perfor- mance and categorization problems. In this scenario, this work highlights a new Brazilian Portuguese LIWC lexicon (LIWC 2015pt), based on LIWC 2015 program. This work compares the performance of LIWC 2007pt and LIWC 2015pt in classification tasks. Three experiments were conducted and the results indi- cate LIWC 2015pt outperforms LIWC 2007pt in all three tasks.

15 citations

Proceedings ArticleDOI
06 Jul 2017
TL;DR: In this article, o presente trabalho tem o objetivo de analisar os textos em portugues do Brasil for inferir a idade dos usuarios.
Abstract: Predadores sociais utilizam a internet para explorar criancas ou adolescentes com propositos abusivos ou sexuais. Cada vez mais esses predadores utilizam as redes sociais para ter acesso as suas vitimas, muitas vezes fornecendo perfis falsos para se passarem por adolescentes. Nesse cenario, o presente trabalho tem o objetivo de analisar os textos em portugues do Brasil para inferir a idade dos usuarios. Para esse proposito, foi utilizada uma ferramenta denominada LIWC em sua versao do portugues do Brasil. Como estudo de caso, foi utilizada uma rede social brasileira para realizar os experimentos. O referido estudo concentrou-se na analise de textos de adolescentes e homens entre 25 e 45 anos, que representam a grande maioria dos predadores sexuais. Os resultados alcancados foram relevantes e abrem lacunas para trabalhos futuros.

5 citations

Proceedings ArticleDOI
17 Oct 2017
TL;DR: This study aims to contribute to the improvement of the process of automatic translation of documents by evaluating the percentage of psycholinguistic changes in automatically translated documents in comparison with a reliable translation performed by a human expert.
Abstract: This work aims at creating a tool for analyzing the psychological and linguistic changes of texts translated from English into Brazilian Portuguese. The aim is to analyze differences between texts translated by automatic translators and human translators. For this purpose, a tool named LIWC is used in its Brazilian Portuguese version. LIWC is a tool that distributes lexical words in categories with linguistic and psychological characteristics. Through accounting the word categories, this work seeks to evaluate the percentage of psycholinguistic changes in automatically translated documents in comparison with a reliable translation performed by a human expert. In this way, this study aims to contribute to the improvement of the process of automatic translation of documents. Experimental results indicate promising directions for further research.

3 citations

Proceedings ArticleDOI
23 Oct 2017
TL;DR: An algorithm is presented to create a hybrid affective lexicon for Brazilian Portuguese based on LIWC and ANEW-Br, which are available in the literature and indicate the potential of the produced lexicon.
Abstract: Studies in human-computer interaction literature concentrate on the problem of extracting affective states from text. However, there is a lack of affective lexicons for Brazilian Portuguese. This study presents an algorithm to create a hybrid affective lexicon for Brazilian Portuguese. The result is based on LIWC and ANEW-Br, which are available in the literature. The experimental results in this work indicate the potential of the produced lexicon.

3 citations

Proceedings ArticleDOI
08 Nov 2016
TL;DR: This work proposes a novel model for personality-based agents to produce different emotional responses and points out that personality traits are relevant to produce emotional agents.
Abstract: Affective Computing is a promising research area with many open challenges. This area expects to develop computational systems that can monitor and respond to the affective states of an interacting user (IU). These affective states can be observed in terms of emotional responses, which demands continuous monitoring of IU. However, emotional responses can vary according to differences in personality traits. Due to that, we propose a novel model for personality-based agents to produce different emotional responses. This model suggests that distinct personality-based agents could have particular emotional responses. To evaluate our model, we collected data from an on-line social network in which entries have personality expressed by users and emotional information associated. We produced two personality-based agents: one extroverted and another introverted. Experimental results indicate that our model can be promising in providing distinct personality-based agents. The extroverted agent performed better on texts written by extroverted individuals just as the introverted agent performed better on texts written by introverted individuals. Our achieved results point out that personality traits are relevant to produce emotional agents.

2 citations


Cited by
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Journal ArticleDOI
TL;DR: Machine learning addresses many of the same research questions as the fields of statistics, data mining, and psychology, but with differences of emphasis.
Abstract: Machine Learning is the study of methods for programming computers to learn. Computers are applied to a wide range of tasks, and for most of these it is relatively easy for programmers to design and implement the necessary software. However, there are many tasks for which this is difficult or impossible. These can be divided into four general categories. First, there are problems for which there exist no human experts. For example, in modern automated manufacturing facilities, there is a need to predict machine failures before they occur by analyzing sensor readings. Because the machines are new, there are no human experts who can be interviewed by a programmer to provide the knowledge necessary to build a computer system. A machine learning system can study recorded data and subsequent machine failures and learn prediction rules. Second, there are problems where human experts exist, but where they are unable to explain their expertise. This is the case in many perceptual tasks, such as speech recognition, hand-writing recognition, and natural language understanding. Virtually all humans exhibit expert-level abilities on these tasks, but none of them can describe the detailed steps that they follow as they perform them. Fortunately, humans can provide machines with examples of the inputs and correct outputs for these tasks, so machine learning algorithms can learn to map the inputs to the outputs. Third, there are problems where phenomena are changing rapidly. In finance, for example, people would like to predict the future behavior of the stock market, of consumer purchases, or of exchange rates. These behaviors change frequently, so that even if a programmer could construct a good predictive computer program, it would need to be rewritten frequently. A learning program can relieve the programmer of this burden by constantly modifying and tuning a set of learned prediction rules. Fourth, there are applications that need to be customized for each computer user separately. Consider, for example, a program to filter unwanted electronic mail messages. Different users will need different filters. It is unreasonable to expect each user to program his or her own rules, and it is infeasible to provide every user with a software engineer to keep the rules up-to-date. A machine learning system can learn which mail messages the user rejects and maintain the filtering rules automatically. Machine learning addresses many of the same research questions as the fields of statistics, data mining, and psychology, but with differences of emphasis. Statistics focuses on understanding the phenomena that have generated the data, often with the goal of testing different hypotheses about those phenomena. Data mining seeks to find patterns in the data that are understandable by people. Psychological studies of human learning aspire to understand the mechanisms underlying the various learning behaviors exhibited by people (concept learning, skill acquisition, strategy change, etc.).

13,246 citations

Proceedings ArticleDOI
12 Apr 2021
TL;DR: In this article, the authors reported the effectiveness of employing automatic text translation methods in automated classification of online discussion messages according to the categories of social and cognitive presences, and highlighted the importance of different features and resources, and the limitations of the resources for Portuguese as reasons of the results obtained.
Abstract: This paper reports the findings of a study that measured the effectiveness of employing automatic text translation methods in automated classification of online discussion messages according to the categories of social and cognitive presences. Specifically, we examined the classification of 1,500 Portuguese and 1,747 English discussion messages using classifiers trained on the datasets before and after the application of text translation. While the English model generated, with the original and translated texts, achieved results (accuracy and Cohen’s κ) similar to those of the previously reported studies, the translation to Portuguese led to a decrease in the performance. The indicates the general viability of the proposed approach when converting the text to English. Moreover, this study highlighted the importance of different features and resources, and the limitations of the resources for Portuguese as reasons of the results obtained.

17 citations

Journal ArticleDOI
TL;DR: The study explored a set of 127 features of online discussion messages and a random forest classifier to automatically recognize the phases of the cognitive presence in online discussion text messages and revealed that the classifier achieved better performance when applied to the entire dataset.
Abstract: This article investigates the impact of educational contexts on automatic classification of online discussion messages according to cognitive presence, an essential construct of the community of inquiry model. In particular, the work reported in the article analyzed online discussion messages written in Brazilian Portuguese from two different courses that were from different subject areas (biology and technology) and had different teaching presence in the online discussions. The study explored a set of 127 features of online discussion messages and a random forest classifier to automatically recognize the phases of the cognitive presence in online discussion messages. The results showed that the classifier achieved better performance when applied to the entire dataset. It reveals that when a classifier is created for a specific course it is not generic enough to be applied to a course from a different field of knowledge. The results also showed the importance of the features that were predictive of the phases of the cognitive presence in the educational context. Based on the findings of this study, future work should adopt the same feature set as used in the current study, but it should train the classifier of the cognitive presence on datasets in subject areas related to the topic of the discussions.

16 citations

Proceedings ArticleDOI
09 Jul 2019
TL;DR: In this paper, a new Brazilian Portuguese LIWC lexicon (LIWC 2015pt) based on LIWC 2015 program is presented, which outperforms LIWC 2007pt in all three tasks.
Abstract: LIWC is a text analysis program that categorizes words into gram- matical and psychologically derived categories. The currently available LIWC lexicon for Brazilian Portuguese (LIWC 2007pt) is based on the 2007 version of LIWC program. As several studies indicated, LIWC 2007pt shows perfor- mance and categorization problems. In this scenario, this work highlights a new Brazilian Portuguese LIWC lexicon (LIWC 2015pt), based on LIWC 2015 program. This work compares the performance of LIWC 2007pt and LIWC 2015pt in classification tasks. Three experiments were conducted and the results indi- cate LIWC 2015pt outperforms LIWC 2007pt in all three tasks.

15 citations

Proceedings ArticleDOI
29 Oct 2019
TL;DR: This work aims to classify Brazilian Portuguese texts to detect hate speech, using data from the Brazilian 55chan imageboard to build a dataset with hate speech content.
Abstract: With the changes in human interaction prompted by the development of communications platforms over the internet, hate speech and offensive language emerged as a contemporary problem. Social networks allow users with different opinions and backgrounds to interact without direct eye-to-eye contact. It brings a sense of safety to promote hate speech, which is even more significant in anonymous environments. There are sites called imageboards, composed of different boards aggregating different topics. On some boards, anonymous users widely promote hate speech. However, only a few works in literature have focused on hate speech in imageboards content. This work aims to classify Brazilian Portuguese texts to detect hate speech, using data from the Brazilian 55chan imageboard to build a dataset with hate speech content. Three classifiers were trained to hate speech binary classification. The Linear Support Vector Classifier achieved the best result with 0.955 of F1-score.

10 citations