358 research outputs found

    Different Lexicon-Based Approaches to Emotion Identification in Portuguese Tweets (Short Paper)

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    This paper presents the existing literature on the identification of emotions and describes various lexica-based approaches and translation strategies to identify emotions in Portuguese tweets. A dataset of tweets was manually annotated to evaluate our classifier and also to assess the difficulty of the task. A lexicon-based approach was used in order to classify the presence or absence of eight different emotions in a tweet. Different strategies have been applied to refine and improve an existing and widely used lexicon, by means of automatic machine translation and aligned word embeddings. We tested six different classification approaches, exploring different ways of directly applying resources available for English by means of different translation strategies. The achieved results suggest that a better performance can be obtained both by improving a lexicon and by directly translating tweets into English and then applying an existing English lexicon

    An application to improve emotional skills in children with Autism Spectrum Disorder

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    Dissertação de Mestrado Integrado em Engenharia de Eletrónica Industrial e ComputadoresThis dissertation presents a project developed with the aim of promoting emotional skills in children with Autism Spectrum Disorders (ASD). The project involves a serious game and a playware object, which is a physical component that acts as the game controller and allows the user to interactively play the serious game. The playware object has six pressure buttons, each one showing an emoji with a specific facial expression and communicates wirelessly via Bluetooth with the serious game app installed in an Android device. The facial expressions used are: happiness, sadness, fear, anger, surprise and neutral/normal. They were applied to the three game activities (imitation, recognition and storytelling). The chain of tests started with an online questionnaire to validate the avatars created to represent the previously mentioned facial expressions in the game (with 114 answers and a mean success rate of 96.2%), which was followed by a usability test of the application (serious game and playware object) with six typically developing children (with 94.4% answer accuracy). Finally, the three game activities were tested with six children with ASD in three/four sessions. Due to the small group test and the short number of sessions, the goal was to test the acceptance of the game rather than the users´ improvement in the activity. It is worth referring that both the serious game and the playware object had a high level of approval from the children and they expressed their interest during the activities. With this project it was intended to contribute to the development of pedagogical resources to be used by professionals and families in the support of children with ASD.Esta dissertação apresenta um projeto desenvolvido com o objetivo de promover capacidades emocionais em crianças com Perturbação do Espectro do Autismo. Este projeto envolve um jogo sério e um objeto playware, que é um componente físico que funciona como controlador de jogo e permite que o utilizador jogue o jogo sério de uma forma interativa. O objeto playware tem seis botões de pressão, cada um com um emoji com uma expressão facial específica, e comunica sem fios por Bluetooth com a aplicação do jogo sério instalada no dispositivo Android. As expressões faciais usadas são: felicidade, tristeza, medo, raiva, surpresa e neutro/normal. Estas foram aplicadas às três diferentes atividades de jogo (imitar, reconhecer e contar histórias). A cadeia de testes começou com um questionário online para validar os avatares criados para representar as expressões faciais previamente mencionadas no jogo (com 114 submissões e uma taxa média de sucesso de 96,2%), seguido de um teste de usabilidade da aplicação (jogo sério e objeto playware) com seis crianças tipicamente desenvolvidas (com 94,4% de respostas corretas). Por fim, as três atividades de jogo foram testadas com seis crianças com Perturbação do Espectro do Autismo durante 3 a 4 sessões. Devido à pequena dimensão do grupo de teste e ao baixo número de sessões, o objetivo foi testar a aceitação do jogo em vez da evolução das capacidades dos utilizadores na atividade. É importante referir que tanto o jogo sério como o objeto playware tiveram um alto nível de aprovação por parte das crianças que expressaram o seu interesse durante as atividades. Este projeto pretende contribuir para o desenvolvimento de recursos pedagógicos a serem usados por profissionais e famílias no apoio a crianças com Perturbação do Espectro do Autismo

    The study of emoji linguistic behaviour: an examination of the theses raised (and not raised) in the academic literature

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    This bibliographic review of academic research on emoji reveals how the bulk of studies accepts it as a language but do not develop detailed linguistic analysis that could support this claim: they accept the clues provided by the initial studies, as if the scientific community had already reached such a consensus. However, the truth is that the fields in which emoji have generated the greatest academic interest (computer science, psychology and cognitive science) have considered the study of their linguistic nature a minor issue. Therefore, research on emoji has been growing over the years, widening the scope of its contributions, but with a common core made up of few basic notions about its linguistic condition that has important blind spots, in which Linguistics hasn’t done (generally) its work to place it in this new context for communication that the digital environments represent, despite the supports provided by multimodality and visual language theory. From these two disciplines, some authors have boldly suggested the emoji’s status as a gesture. However, to analyse its linguistic nature and behaviour, it is more accurate to understand the emoji, not as a gesture, but as a simplified representation of a gesture, without the unique features that a personal gesture has. The emoji seems to be the tool that, with fewer resources, best ensures that the interlocutor can understand the intentionality with which the sender has written the message

    Recognizing Emotions in Short Texts

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    Tese de mestrado, Ciência Cognitiva, Universidade de Lisboa, Faculdade de Ciências, 2022O reconhecimento automático de emoções em texto é uma tarefa que mobiliza as áreas de processamento de linguagem natural e de computação afetiva, para as quais se pode contar com o especial contributo de disciplinas da Ciência Cognitiva como Inteligência Artificial e Ciência da Computação, Linguística e Psicologia. Visa, sobretudo, a deteção e interpretação de emoções humanas através da sua expressão na forma escrita por sistemas computacionais. A interação entre processos afetivos e cognitivos, o papel essencial que as emoções desempenham nas interações interpessoais e a crescente utilização de comunicação escrita online nos dias de hoje fazem com que o reconhecimento de emoções de forma automática seja cada vez mais importante, nomeadamente em áreas como saúde mental, interação pessoa-computador, ciência política ou marketing. A língua inglesa tem sido o maior alvo de estudo no que diz respeito ao reconhecimento de emoções em textos, sendo que ainda existe pouco trabalho desenvolvido para a língua portuguesa. Assim, existe uma necessidade em expandir o trabalho feito para a língua inglesa para o português. Esta dissertação tem como objetivo a comparação de dois métodos distintos de aprendizagem profunda resultantes dos avanços na área de Inteligência Artificial para detetar e classificar de forma automática estados emocionais discretos em textos escritos em língua portuguesa. Para tal, a abordagem de classificação de Polignano et al. (2019) baseada em redes de aprendizagem profunda como Long Short-Term Memory bidirecionais e redes convolucionais mediadas por um mecanismo de atenção será replicada para a língua inglesa e será reproduzida para a língua portuguesa. Para a língua inglesa, será utilizado o conjunto de dados da tarefa 1 do SemEval-2018 (Mohammad et al., 2018) tal como na experiência original, que considera quatro emoções discretas: raiva, medo, alegria e tristeza. Para a língua portuguesa, tendo em consideração a falta de conjuntos de dados disponíveis anotados relativamente a emoções, será efetuada uma recolha de dados a partir da rede social Twitter recorrendo a hashtags com conteúdo associado a uma emoção específica para determinar a emoção subjacente ao texto de entre as mesmas quatro emoções presentes no conjunto de dados da língua inglesa que será utilizado. De acordo com experiências realizadas por Mohammad & Kiritchenko (2015), este método de recolha de dados é consistente com a anotação de juízes humanos treinados. Tendo em conta a rápida e contínua evolução dos métodos de aprendizagem profunda para o processamento de linguagem natural e o estado da arte estabelecido por métodos recentes em tarefas desta área tal como o modelo pré-treinado BERT (Bidirectional Encoder Representations from Tranformers) (Devlin et al., 2019), será também aplicada esta abordagem para a tarefa de reconhecimento de emoções para as duas línguas em questão, utilizando os mesmos conjuntos de dados das experiências anteriores. Enquanto a abordagem de Polignano et al. teve um melhor desempenho nas experiências que realizámos com dados em inglês, com diferenças de F1-score de 0.02, o melhor resultado obtido nas experiências com dados na língua portuguesa foi com o modelo BERT, obtendo um resultado máximo de F1-score de 0.6124.Automatic emotion recognition from text is a task that mobilizes the areas of natural language processing and affective computing counting with the special contribution of Cognitive Science subjects such as Artificial Intelligence and Computer Science, Linguistics and Psychology. It aims at the detection and interpretation of human emotions expressed in the written form by computational systems. The interaction of affective and cognitive processes, the essential role that emotions play in interpersonal interactions and the currently increasing use of written communication online make automatic emotion recognition progressively important, namely in areas such as mental healthcare, human-computer interaction, political science, or marketing. The English language has been the main target of studies in emotion recognition in text and the work developed for the Portuguese language is still scarce. Thus, there is a need to expand the work developed for English to Portuguese. The goal of this dissertation is to present and compare two distinct deep learning methods resulting from the advances in Artificial Intelligence to automatically detect and classify discrete emotional states in texts written in Portuguese. For this, the classification approach of Polignano et al. (2019) based on deep learning networks such as bidirectional Long Short-Term Memory and convolutional networks mediated by a self-attention level will be replicated for English and it will be reproduced for Portuguese. For English, the SemEval-2018 task 1 dataset (Mohammad et al., 2018) will be used, as in the original experience, and it considers four discrete emotions: anger, fear, joy, and sadness. For Portuguese, considering the lack of available emotionally annotated datasets, data will be collected from the social network Twitter using hashtags associated to a specific emotional content to determine the underlying emotion of the text from the same four emotions present in the English dataset. According to experiments carried out by Mohammad & Kiritchenko (2015), this method of data collection is consistent with the annotation of trained human judges. Considering the fast and continuous evolution of deep learning methods for natural language processing and the state-of-the-art results achieved by recent methods in tasks in this area such as the pre-trained language model BERT (Bidirectional Encoder Representations from Transformers) (Devlin et al., 2019), this approach will also be applied to the task of emotion recognition for both languages using the same datasets from the previous experiments. It is expected to draw conclusions about the adequacy of these two presented approaches in emotion recognition and to contribute to the state of the art in this task for the Portuguese language. While the approach of Polignano et al. had a better performance in the experiments with English data with a difference in F1 scores of 0.02, for Portuguese we obtained the best result with BERT having a maximum F1 score of 0.6124

    The study of emoji linguistic behaviour: an examination of the theses raised (and not raised) in the academic literature

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    This bibliographic review of academic research on emoji reveals how the bulk of studies accepts it as a language but do not develop detailed linguistic analysis that could support this claim: they accept the clues provided by the initial studies, as if the scientific community had already reached such a consensus. However, the truth is that the fields in which emoji have generated the greatest academic interest (computer science, psychology and cognitive science) have considered the study of their linguistic nature a minor issue. Therefore, research on emoji has been growing over the years, widening the scope of its contributions, but with a common core made up of few basic notions about its linguistic condition that has important blind spots, in which Linguistics hasn’t done (generally) its work to place it in this new context for communication that the digital environments represent, despite the supports provided by multimodality and visual language theory. From these two disciplines, some authors have boldly suggested the emoji’s status as a gesture. However, to analyse its linguistic nature and behaviour, it is more accurate to understand the emoji, not as a gesture, but as a simplified representation of a gesture, without the unique features that a personal gesture has. The emoji seems to be the tool that, with fewer resources, best ensures that the interlocutor can understand the intentionality with which the sender has written the message

    Detección de discurso de odio online utilizando Machine Learning

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    Trabajo de Fin de Grado en Ingeniería informática, Facultad de Informática UCM, Departamento de Ingeniería del Software e Inteligencia Artificial, Curso 2021/2022. Enlace al repositorio público del proyecto: https://github.com/NILGroup/TFG-2122HateSpeechDetectionHate speech directed towards marginalized people is a very common problem online, especially in social media such as Twitter or Reddit. Automatically detecting hate speech in such spaces can help mend the Internet and transform it into a safer environment for everybody. Hate speech detection fits into text classification, a series of tasks where text is organized into categories. This project2 proposes using Machine Learning algorithms to detect hate speech in online text in four languages: English, Spanish, Italian and Portuguese. The data to train the models was obtained from online, publicly available datasets. Three different algorithms with varying parameters have been used in order to compare their performance. The experiments show that the best results reach an 82.51% accuracy and around an 83% F1-score, for Italian text. Each language has different results depending on distinct factors.El discurso de odio dirigido a personas marginadas es un problema muy común en línea, especialmente en redes sociales como Twitter o Reddit. La detección automática del discurso de odio en dichos espacios puede ayudar a reparar Internet y a transformarlo en un entorno más seguro para todos. La detección del discurso de odio encaja en la clasificación de texto, donde se organiza en categorías. Este proyecto1 propone el uso de algoritmos de Machine Learning para localizar discurso de odio en textos online en cuatro idiomas: inglés, español, italiano y portugués. Los datos para entrenar los modelos se obtuvieron de datasets disponibles públicamente en línea. Se han utilizado tres algoritmos diferentes con distintos parámetros para comparar su rendimiento. Los experimentos muestran que los mejores resultados alcanzan una precisión del 82,51 % y un valor F1 de alrededor del 83 % en italiano. Los resultados para cada idioma varían dependiendo de distintos factores.Depto. de Ingeniería de Software e Inteligencia Artificial (ISIA)Fac. de InformáticaTRUEunpu

    Motives, frequency and attitudes toward emoji and emoticon use

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    Electronic Mediated Communication (EMC) has become highly prevalent in our daily lives. Many of the communication formats used in EMC are text-based (e.g., instant messaging), and users often include visual paralinguistic cues in their messages. In the current study, we examined the usage of two of such cues - emoji and emoticons. Specifically, we compared self-reported frequency of use, as well as attitudes (6 bipolar items, e.g., “fun” vs. “boring”) and motives for their usage (9 motives, e.g., “express how I feel to others”). We also examined these indicators according to age and gender. Overall, participants (N = 474, 72.6% women; Mage = 30.71, SD = 12.58) reported using emoji (vs. emoticons) more often, revealed more positive attitudes toward emoji usage, and identified more with motives to use them. Moreover, all the ratings were higher among younger (vs. older) participants. Results also showed that women reported to use emoji (but not emoticons) more often and expressed more positive attitudes toward their usage than men. However, these gender differences were particularly evident for younger participants. No gender differences were found for emoticons usage. These findings add to the emerging body of literature by showing the relevance of considering age and gender, and their interplay, when examining patterns of emoji and emoticons use.info:eu-repo/semantics/acceptedVersio

    Insights on the use and consequences of user-generated content in the context of portuguese luxury hotels

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    Facebook have captured a special interest from private users and business organizations. In parallel, the concept of user-generated content has appeared as an active marketing tool since Facebook users could spread the word and share opinions on their pages with their "so-called" Facebook friends. This study aims to identify and understand the strategies and purposes of luxury hotel brand followers on Facebook towards user-generated content. 355 user-generated posts from 50 valid hotel’s brand pages have been collected from Facebook and coded manually for each variable. Moreover, posts were treated as a respondent and were coded according several variables such gender, nationality, post type, etc. To get better results, a descriptive analysis was performed. The results showed that pots with images were the most chosen post type to share any type of content and the favorite content types were sharing moments from holidays and writing guest reviews. As to the best day to post content, each gender has its own preferences: for females are the weekends while males favor Wednesdays and Fridays. However, 83,4% of the brand pages did not engage with generated posts and the involvement only occurred with guest reviews and holidays’ moments. The results give insight about the strategies adopted by Facebook users to generate content on their profiles about a specific hotel brand and begin to fill the gap on user-generated content utilization by hotel managers.O Facebook tem captado um interesse especial, tanto dos utilizadores particulares como das organizações. Consequentemente, o conceito de conteúdo criado pelos utilizadores apareceu como uma ferramenta de marketing, uma vez que os utilizadores com perfil de Facebook podem partilhar as suas opiniões nos seus próprios perfis com seus "amigos". Este estudo pretende identificar e compreender, as estratégias e intenções, dos seguidores de páginas de marcas hoteleiras de luxo no Facebook, através do conteúdo gerado pelos utilizadores. Extraímos 355 posts criados por utilizadores de 50 páginas de marcas de hotéis de luxo, tendo sido recolhidos no Facebook, e codificados manualmente para cada variável. Assim, foram tratados como uma resposta e codificados por: género, nacionalidade, tipo de post, etc. Para uma melhor compreensão dos resultados, foi realizado uma análise descritiva. Concluiu-se que os posts com imagens são os mais escolhidos para partilhar qualquer tipo de conteúdo, e que, os conteúdos favoritos mostravam momentos em férias, e mostravam críticas feitas por hóspedes. Relativamente aos melhores dias para fazer publicações, cada género tem as suas preferências: as mulheres preferem os fins-de semanas enquanto que os homens as quartas e sextas-feiras. Contudo, 83,4% das páginas analisadas não mostraram qualquer interacção com os utilizadores, e tal só aconteceu em críticas de hóspedes e férias. Os resultados dão uma visão sobre as estratégias adoptadas pelos utilizadores de Facebook para criar conteúdo nos seus perfis sobre uma marca de hotel específica, e começar a preencher a lacuna na utilização desta ferramenta pelos gestores das páginas de marcas hoteleiras

    Using emoji in an e-commerce context: effects in brand perception, quality of service and intention to recommend

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    The increasing development of digital technologies has lead to business model disruption, transformation and developed new means of providing services and products (e.g., e-banking and e-commerce). In order to cope with, and benefit from these changes, people have changed their habits. Such is the case of Electronic Mediated Communication (EMC) that changed how and what people communicate (e.g., Skype, e-mail). Text-centric EMC (e.g., IM, e-mails) has itself evolved to allow the expression of emotion between sender and receiver, namely through the use of emoji. However how service providers and brands relate with their progressively more digital customer base in electronic contexts, and what may be the outcomes of that relationship is still an unexplored area of research. In the present work, we present two experiments that aimed to examine the influence of emoji use in brand-consumer communication during an online ticket selling transaction, on consumers perception of brand, quality of service and intention to recommend. Besides manipulating emoji presence (or absence), we additionally manipulated message valence (e-commerce transaction success or failure - Experiment 1) and product scarcity (Experiment 2). Overall, results suggest that guaranteeing service success is more determinant of brand and quality of service evaluation than the type of language used. Specifically, in Experiment 1, emoji use seems to have influenced the perception of language informality, while in Experiment 2, seems to have influenced the perception of brands social presence and warmth, language informality and funniness, as well as quality of service.A evolução das tecnologias digitais levou ao surgimento de novos modelos de negócio, e formas de fornecer serviços e produtos (e.g., e-commerce). Adaptando-se a estas mudanças, as pessoas alteraram parte dos seus hábitos. Um exemplo é o caso da Comunicação mediada por meio Eletrónicos (EMC), que mudou a forma e o conteúdo do que as pessoas comunicam (e.g., Skype, e-mail). A EMC em formato de texto (e.g., e-mails) evoluiu, permitindo a expressão da emoção entre emissor e recetor, nomeadamente através da utilização de emoji. No entanto, a forma como marcas e prestadores de serviço se relacionam com clientes em contextos eletrónicos, e quais os possíveis resultados desse relacionamento, é uma área de pesquisa pouco explorada. Neste trabalho apresentamos dois experimentos que examinam a influência do uso de emoji na comunicação da marca-consumidor durante uma venda de bilhetes on-line, na perceção da marca, qualidade do serviço e intenção de recomendar dos consumidores. Para além da presença (ausência) de emoji, manipulámos a valência da mensagem (sucesso ou insucesso da transação - Experimento 1) e a disponibilidade de produto (Experimento 2). Globalmente, os resultados sugerem que garantir o sucesso de serviço é mais determinante para a avaliação de marca e qualidade de serviço do que o tipo de linguagem utilizado. Especificamente, no Experimento 1, o uso de emoji parece influenciar a perceção de informalidade da linguagem, enquanto no Experimento 2, parece ter influenciado a perceção de presença social e calor da marca, de linguagem ser informal e divertida, assim como qualidade de serviço
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