Neural Cognition and Affective Computing on Cyber Language

Characterized by its customary symbol system and simple and vivid expression patterns, cyber language acts as not only a tool for convenient communication but also a carrier of abundant emotions and causes high attention in public opinion analysis, internet marketing, service feedback monitoring, and social emergency management. Based on our multidisciplinary research, this paper presents a classification of the emotional symbols in cyber language, analyzes the cognitive characteristics of different symbols, and puts forward a mechanism model to show the dominant neural activities in that process. Through the comparative study of Chinese, English, and Spanish, which are used by the largest population in the world, this paper discusses the expressive patterns of emotions in international cyber languages and proposes an intelligent method for affective computing on cyber language in a unified PAD (Pleasure-Arousal-Dominance) emotional space

A Review on MAS-Based Sentiment and Stress Analysis User-Guiding and Risk-Prevention Systems in Social Network Analysis

[EN] In the current world we live immersed in online applications, being one of the most present of them Social Network Sites (SNSs), and different issues arise from this interaction. Therefore, there is a need for research that addresses the potential issues born from the increasing user interaction when navigating. For this reason, in this survey we explore works in the line of prevention of risks that can arise from social interaction in online environments, focusing on works using Multi-Agent System (MAS) technologies. For being able to assess what techniques are available for prevention, works in the detection of sentiment polarity and stress levels of users in SNSs will be reviewed. We review with special attention works using MAS technologies for user recommendation and guiding. Through the analysis of previous approaches on detection of the user state and risk prevention in SNSs we elaborate potential future lines of work that might lead to future applications where users can navigate and interact between each other in a more safe way.This work was funded by the project TIN2017-89156-R of the Spanish government.Aguado-Sarrió, G.; Julian Inglada, VJ.; García-Fornes, A.; Espinosa Minguet, AR. (2020). A Review on MAS-Based Sentiment and Stress Analysis User-Guiding and Risk-Prevention Systems in Social Network Analysis. Applied Sciences. 10(19):1-29. https://doi.org/10.3390/app10196746S1291019Vanderhoven, E., Schellens, T., Vanderlinde, R., & Valcke, M. (2015). Developing educational materials about risks on social network sites: a design based research approach. Educational Technology Research and Development, 64(3), 459-480. doi:10.1007/s11423-015-9415-4Teens and ICT: Risks and Opportunities. 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Sparse Autoencoder-Based Feature Transfer Learning for Speech Emotion Recognition. 2013 Humaine Association Conference on Affective Computing and Intelligent Interaction. doi:10.1109/acii.2013.90Nicolaou, M. A., Gunes, H., & Pantic, M. (2011). Continuous Prediction of Spontaneous Affect from Multiple Cues and Modalities in Valence-Arousal Space. IEEE Transactions on Affective Computing, 2(2), 92-105. doi:10.1109/t-affc.2011.9Hossain, M. S., Muhammad, G., Alhamid, M. F., Song, B., & Al-Mutib, K. (2016). Audio-Visual Emotion Recognition Using Big Data Towards 5G. Mobile Networks and Applications, 21(5), 753-763. doi:10.1007/s11036-016-0685-9Zhou, F., Jianxin Jiao, R., & Linsey, J. S. (2015). Latent Customer Needs Elicitation by Use Case Analogical Reasoning From Sentiment Analysis of Online Product Reviews. Journal of Mechanical Design, 137(7). doi:10.1115/1.4030159Ceci, F., Goncalves, A. L., & Weber, R. (2016). A model for sentiment analysis based on ontology and cases. IEEE Latin America Transactions, 14(11), 4560-4566. doi:10.1109/tla.2016.7795829Vizer, L. M., Zhou, L., & Sears, A. (2009). Automated stress detection using keystroke and linguistic features: An exploratory study. International Journal of Human-Computer Studies, 67(10), 870-886. doi:10.1016/j.ijhcs.2009.07.005Feldman, R. (2013). Techniques and applications for sentiment analysis. Communications of the ACM, 56(4), 82-89. doi:10.1145/2436256.2436274Schouten, K., & Frasincar, F. (2016). Survey on Aspect-Level Sentiment Analysis. IEEE Transactions on Knowledge and Data Engineering, 28(3), 813-830. doi:10.1109/tkde.2015.2485209Ji, R., Cao, D., Zhou, Y., & Chen, F. (2016). Survey of visual sentiment prediction for social media analysis. Frontiers of Computer Science, 10(4), 602-611. doi:10.1007/s11704-016-5453-2Li, L., Cao, D., Li, S., & Ji, R. (2015). Sentiment analysis of Chinese micro-blog based on multi-modal correlation model. 2015 IEEE International Conference on Image Processing (ICIP). doi:10.1109/icip.2015.7351718Lee, P.-M., Tsui, W.-H., & Hsiao, T.-C. (2015). The Influence of Emotion on Keyboard Typing: An Experimental Study Using Auditory Stimuli. PLOS ONE, 10(6), e0129056. doi:10.1371/journal.pone.0129056Matsiola, M., Dimoulas, C., Kalliris, G., & Veglis, A. A. (2018). Augmenting User Interaction Experience Through Embedded Multimodal Media Agents in Social Networks. Information Retrieval and Management, 1972-1993. doi:10.4018/978-1-5225-5191-1.ch088Rosaci, D. (2007). CILIOS: Connectionist inductive learning and inter-ontology similarities for recommending information agents. Information Systems, 32(6), 793-825. doi:10.1016/j.is.2006.06.003Buccafurri, F., Comi, A., Lax, G., & Rosaci, D. (2016). Experimenting with Certified Reputation in a Competitive Multi-Agent Scenario. 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The European Physical Journal B, 77(4), 533-545. doi:10.1140/epjb/e2010-00292-

Implicit emotion detection in text

In text, emotion can be expressed explicitly, using emotion-bearing words (e.g. happy, guilty) or implicitly without emotion-bearing words. Existing approaches focus on the detection of explicitly expressed emotion in text. However, there are various ways to express and convey emotions without the use of these emotion-bearing words. For example, given two sentences: “The outcome of my exam makes me happy” and “I passed my exam”, both sentences express happiness, with the first expressing it explicitly and the other implying it. In this thesis, we investigate implicit emotion detection in text. We propose a rule-based approach for implicit emotion detection, which can be used without labeled corpora for training. Our results show that our approach outperforms the lexicon matching method consistently and gives competitive performance in comparison to supervised classifiers. Given that emotions such as guilt and admiration which often require the identification of blameworthiness and praiseworthiness, we also propose an approach for the detection of blame and praise in text, using an adapted psychology model, Path model to blame. Lack of benchmarking dataset led us to construct a corpus containing comments of individuals’ emotional experiences annotated as blame, praise or others. Since implicit emotion detection might be useful for conflict-of-interest (CoI) detection in Wikipedia articles, we built a CoI corpus and explored various features including linguistic and stylometric, presentation, bias and emotion features. Our results show that emotion features are important when using Nave Bayes, but the best performance is obtained with SVM on linguistic and stylometric features only. Overall, we show that a rule-based approach can be used to detect implicit emotion in the absence of labelled data; it is feasible to adopt the psychology path model to blame for blame/praise detection from text, and implicit emotion detection is beneficial for CoI detection in Wikipedia articles

Natural Language Processing using Deep Learning in Social Media

[ES] En los últimos años, los modelos de aprendizaje automático profundo (AP) han revolucionado los sistemas de procesamiento de lenguaje natural (PLN). Hemos sido testigos de un avance formidable en las capacidades de estos sistemas y actualmente podemos encontrar sistemas que integran modelos PLN de manera ubicua. Algunos ejemplos de estos modelos con los que interaccionamos a diario incluyen modelos que determinan la intención de la persona que escribió un texto, el sentimiento que pretende comunicar un tweet o nuestra ideología política a partir de lo que compartimos en redes sociales. En esta tesis se han propuestos distintos modelos de PNL que abordan tareas que estudian el texto que se comparte en redes sociales. En concreto, este trabajo se centra en dos tareas fundamentalmente: el análisis de sentimientos y el reconocimiento de la personalidad de la persona autora de un texto. La tarea de analizar el sentimiento expresado en un texto es uno de los problemas principales en el PNL y consiste en determinar la polaridad que un texto pretende comunicar. Se trata por lo tanto de una tarea estudiada en profundidad de la cual disponemos de una vasta cantidad de recursos y modelos. Por el contrario, el problema del reconocimiento de personalidad es una tarea revolucionaria que tiene como objetivo determinar la personalidad de los usuarios considerando su estilo de escritura. El estudio de esta tarea es más marginal por lo que disponemos de menos recursos para abordarla pero que no obstante presenta un gran potencial. A pesar de que el enfoque principal de este trabajo fue el desarrollo de modelos de aprendizaje profundo, también hemos propuesto modelos basados en recursos lingüísticos y modelos clásicos del aprendizaje automático. Estos últimos modelos nos han permitido explorar las sutilezas de distintos elementos lingüísticos como por ejemplo el impacto que tienen las emociones en la clasificación correcta del sentimiento expresado en un texto. Posteriormente, tras estos trabajos iniciales se desarrollaron modelos AP, en particular, Redes neuronales convolucionales (RNC) que fueron aplicadas a las tareas previamente citadas. En el caso del reconocimiento de la personalidad, se han comparado modelos clásicos del aprendizaje automático con modelos de aprendizaje profundo, pudiendo establecer una comparativa bajo las mismas premisas. Cabe destacar que el PNL ha evolucionado drásticamente en los últimos años gracias al desarrollo de campañas de evaluación pública, donde múltiples equipos de investigación comparan las capacidades de los modelos que proponen en las mismas condiciones. La mayoría de los modelos presentados en esta tesis fueron o bien evaluados mediante campañas de evaluación públicas, o bien emplearon la configuración de una campaña pública previamente celebrada. Siendo conscientes, por lo tanto, de la importancia de estas campañas para el avance del PNL, desarrollamos una campaña de evaluación pública cuyo objetivo era clasificar el tema tratado en un tweet, para lo cual recogimos y etiquetamos un nuevo conjunto de datos. A medida que avanzabamos en el desarrollo del trabajo de esta tesis, decidimos estudiar en profundidad como las RNC se aplicaban a las tareas de PNL. En este sentido, se exploraron dos líneas de trabajo. En primer lugar, propusimos un método de relleno semántico para RNC, que plantea una nueva manera de representar el texto para resolver tareas de PNL. Y en segundo lugar, se introdujo un marco teórico para abordar una de las críticas más frecuentes del aprendizaje profundo, el cual es la falta de interpretabilidad. Este marco busca visualizar qué patrones léxicos, si los hay, han sido aprendidos por la red para clasificar un texto.[CA] En els últims anys, els models d'aprenentatge automàtic profund (AP) han revolucionat els sistemes de processament de llenguatge natural (PLN). Hem estat testimonis d'un avanç formidable en les capacitats d'aquests sistemes i actualment podem trobar sistemes que integren models PLN de manera ubiqua. Alguns exemples d'aquests models amb els quals interaccionem diàriament inclouen models que determinen la intenció de la persona que va escriure un text, el sentiment que pretén comunicar un tweet o la nostra ideologia política a partir del que compartim en xarxes socials. En aquesta tesi s'han proposats diferents models de PNL que aborden tasques que estudien el text que es comparteix en xarxes socials. En concret, aquest treball se centra en dues tasques fonamentalment: l'anàlisi de sentiments i el reconeixement de la personalitat de la persona autora d'un text. La tasca d'analitzar el sentiment expressat en un text és un dels problemes principals en el PNL i consisteix a determinar la polaritat que un text pretén comunicar. Es tracta per tant d'una tasca estudiada en profunditat de la qual disposem d'una vasta quantitat de recursos i models. Per contra, el problema del reconeixement de la personalitat és una tasca revolucionària que té com a objectiu determinar la personalitat dels usuaris considerant el seu estil d'escriptura. L'estudi d'aquesta tasca és més marginal i en conseqüència disposem de menys recursos per abordar-la però no obstant i això presenta un gran potencial. Tot i que el fouc principal d'aquest treball va ser el desenvolupament de models d'aprenentatge profund, també hem proposat models basats en recursos lingüístics i models clàssics de l'aprenentatge automàtic. Aquests últims models ens han permès explorar les subtileses de diferents elements lingüístics com ara l'impacte que tenen les emocions en la classificació correcta del sentiment expressat en un text. Posteriorment, després d'aquests treballs inicials es van desenvolupar models AP, en particular, Xarxes neuronals convolucionals (XNC) que van ser aplicades a les tasques prèviament esmentades. En el cas de el reconeixement de la personalitat, s'han comparat models clàssics de l'aprenentatge automàtic amb models d'aprenentatge profund la qual cosa a permet establir una comparativa de les dos aproximacions sota les mateixes premisses. Cal remarcar que el PNL ha evolucionat dràsticament en els últims anys gràcies a el desenvolupament de campanyes d'avaluació pública on múltiples equips d'investigació comparen les capacitats dels models que proposen sota les mateixes condicions. La majoria dels models presentats en aquesta tesi van ser o bé avaluats mitjançant campanyes d'avaluació públiques, o bé s'ha emprat la configuració d'una campanya pública prèviament celebrada. Sent conscients, per tant, de la importància d'aquestes campanyes per a l'avanç del PNL, vam desenvolupar una campanya d'avaluació pública on l'objectiu era classificar el tema tractat en un tweet, per a la qual cosa vam recollir i etiquetar un nou conjunt de dades. A mesura que avançàvem en el desenvolupament del treball d'aquesta tesi, vam decidir estudiar en profunditat com les XNC s'apliquen a les tasques de PNL. En aquest sentit, es van explorar dues línies de treball.En primer lloc, vam proposar un mètode d'emplenament semàntic per RNC, que planteja una nova manera de representar el text per resoldre tasques de PNL. I en segon lloc, es va introduir un marc teòric per abordar una de les crítiques més freqüents de l'aprenentatge profund, el qual és la falta de interpretabilitat. Aquest marc cerca visualitzar quins patrons lèxics, si n'hi han, han estat apresos per la xarxa per classificar un text.[EN] In the last years, Deep Learning (DL) has revolutionised the potential of automatic systems that handle Natural Language Processing (NLP) tasks. We have witnessed a tremendous advance in the performance of these systems. Nowadays, we found embedded systems ubiquitously, determining the intent of the text we write, the sentiment of our tweets or our political views, for citing some examples. In this thesis, we proposed several NLP models for addressing tasks that deal with social media text. Concretely, this work is focused mainly on Sentiment Analysis and Personality Recognition tasks. Sentiment Analysis is one of the leading problems in NLP, consists of determining the polarity of a text, and it is a well-known task where the number of resources and models proposed is vast. In contrast, Personality Recognition is a breakthrough task that aims to determine the users' personality using their writing style, but it is more a niche task with fewer resources designed ad-hoc but with great potential. Despite the fact that the principal focus of this work was on the development of Deep Learning models, we have also proposed models based on linguistic resources and classical Machine Learning models. Moreover, in this more straightforward setup, we have explored the nuances of different language devices, such as the impact of emotions in the correct classification of the sentiment expressed in a text. Afterwards, DL models were developed, particularly Convolutional Neural Networks (CNNs), to address previously described tasks. In the case of Personality Recognition, we explored the two approaches, which allowed us to compare the models under the same circumstances. Noteworthy, NLP has evolved dramatically in the last years through the development of public evaluation campaigns, where multiple research teams compare the performance of their approaches under the same conditions. Most of the models here presented were either assessed in an evaluation task or either used their setup. Recognising the importance of this effort, we curated and developed an evaluation campaign for classifying political tweets. In addition, as we advanced in the development of this work, we decided to study in-depth CNNs applied to NLP tasks. Two lines of work were explored in this regard. Firstly, we proposed a semantic-based padding method for CNNs, which addresses how to represent text more appropriately for solving NLP tasks. Secondly, a theoretical framework was introduced for tackling one of the most frequent critics of Deep Learning: interpretability. This framework seeks to visualise what lexical patterns, if any, the CNN is learning in order to classify a sentence. In summary, the main achievements presented in this thesis are: - The organisation of an evaluation campaign for Topic Classification from texts gathered from social media. - The proposal of several Machine Learning models tackling the Sentiment Analysis task from social media. Besides, a study of the impact of linguistic devices such as figurative language in the task is presented. - The development of a model for inferring the personality of a developer provided the source code that they have written. - The study of Personality Recognition tasks from social media following two different approaches, models based on machine learning algorithms and handcrafted features, and models based on CNNs were proposed and compared both approaches. - The introduction of new semantic-based paddings for optimising how the text was represented in CNNs. - The definition of a theoretical framework to provide interpretable information to what CNNs were learning internally.Giménez Fayos, MT. (2021). Natural Language Processing using Deep Learning in Social Media [Tesis doctoral]. Universitat Politècnica de València. https://doi.org/10.4995/Thesis/10251/172164TESI

Sentiment Analysis for Social Media

Sentiment analysis is a branch of natural language processing concerned with the study of the intensity of the emotions expressed in a piece of text. The automated analysis of the multitude of messages delivered through social media is one of the hottest research fields, both in academy and in industry, due to its extremely high potential applicability in many different domains. This Special Issue describes both technological contributions to the field, mostly based on deep learning techniques, and specific applications in areas like health insurance, gender classification, recommender systems, and cyber aggression detection

Stance Prediction for Russian: Data and Analysis

Stance detection is a critical component of rumour and fake news identification. It involves the extraction of the stance a particular author takes related to a given claim, both expressed in text. This paper investigates stance classification for Russian. It introduces a new dataset, RuStance, of Russian tweets and news comments from multiple sources, covering multiple stories, as well as text classification approaches to stance detection as benchmarks over this data in this language. As well as presenting this openly-available dataset, the first of its kind for Russian, the paper presents a baseline for stance prediction in the language

Computing point-of-view : modeling and simulating judgments of taste

Thesis (Ph. D.)--Massachusetts Institute of Technology, School of Architecture and Planning, Program in Media Arts and Sciences, 2006.Includes bibliographical references (p. 153-163).People have rich points-of-view that afford them the ability to judge the aesthetics of people, things, and everyday happenstance; yet viewpoint has an ineffable quality that is hard to articulate in words, let alone capture in computer models. Inspired by cultural theories of taste and identity, this thesis explores end-to-end computational modeling of people's tastes-from model acquisition, to generalization, to application- under various realms. Five aesthetical realms are considered-cultural taste, attitudes, ways of perceiving, taste for food, and sense-of-humor. A person's model is acquired by reading her personal texts, such as a weblog diary, a social network profile, or emails. To generalize a person model, methods such as spreading activation, analogy, and imprimer supplementation are applied to semantic resources and search spaces mined from cultural corpora. Once a generalized model is achieved, a person's tastes are brought to life through perspective-based applications, which afford the exploration of someone else's perspective through interactivity and play. The thesis describes model acquisition systems implemented for each of the five aesthetical realms.(cont.) The techniques of 'reading for affective themes' (RATE), and 'culture mining' are described, along with their enabling technologies, which are commonsense reasoning and textual affect analysis. Finally, six perspective-based applications were implemented to illuminate a range of real-world beneficiaries to person modeling-virtual mentoring, self-reflection, and deep customization.by Xinyu Hugo Liu.Ph.D

Event Detection and Tracking Detection of Dangerous Events on Social Media

Online social media platforms have become essential tools for communication and information exchange in our lives. It is used for connecting with people and sharing information. This phenomenon has been intensively studied in the past decade to investigate users’ sentiments for different scenarios and purposes. As the technology advanced and popularity increased, it led to the use of different terms referring to similar topics which often result in confusion. We study such trends and intend to propose a uniform solution that deals with the subject clearly. We gather all these ambiguous terms under the umbrella of the most recent and popular terms to reach a concise verdict. Many events have been addressed in recent works that cover only specific types and domains of events. For the sake of keeping things simple and practical, the events that are extreme, negative, and dangerous are grouped under the name Dangerous Events (DE). These dangerous events are further divided into three main categories of action-based, scenario-based, and sentiments-based dangerous events to specify their characteristics. We then propose deep-learning-based models to detect events that are dangerous in nature. The deep-learning models that include BERT, RoBERTa, and XLNet provide valuable results that can effectively help solve the issue of detecting dangerous events using various dimensions. Even though the models perform well, the main constraint of fewer available event datasets and lower quality of certain events data affects the performance of these models can be tackled by handling the issue accordingly.As plataformas online de redes sociais tornaram-se ferramentas essenciais para a comunicação, conexão com outros, e troca de informação nas nossas vidas. Este fenómeno tem sido intensamente estudado na última década para investigar os sentimentos dos utilizadores em diferentes cenários e para vários propósitos. Contudo, a utilização dos meios de comunicação social tornou-se mais complexa e num fenómeno mais vasto devido ao envolvimento de múltiplos intervenientes, tais como empresas, grupos e outras organizações. À medida que a tecnologia avançou e a popularidade aumentou, a utilização de termos diferentes referentes a tópicos semelhantes gerou confusão. Por outras palavras, os modelos são treinados segundo a informação de termos e âmbitos específicos. Portanto, a padronização é imperativa. O objetivo deste trabalho é unir os diferentes termos utilizados em termos mais abrangentes e padronizados. O perigo pode ser uma ameaça como violência social, desastres naturais, danos intelectuais ou comunitários, contágio, agitação social, perda económica, ou apenas a difusão de ideologias odiosas e violentas. Estudamos estes diferentes eventos e classificamos-los em tópicos para que a ténica de deteção baseada em tópicos possa ser concebida e integrada sob o termo Evento Perigosos (DE). Consequentemente, definimos o termo proposto “Eventos Perigosos” (Dangerous Events) e dividimo-lo em três categorias principais de modo a especificar as suas características. Sendo estes denominados Eventos Perigosos, Eventos Perigosos de nível superior, e Eventos Perigosos de nível inferior. O conjunto de dados MAVEN foi utilizado para a obtenção de conjuntos de dados para realizar a experiência. Estes conjuntos de dados são filtrados manualmente com base no tipo de eventos para separar eventos perigosos de eventos gerais. Os modelos de transformação BERT, RoBERTa, e XLNet foram utilizados para classificar dados de texto consoante a respetiva categoria de Eventos Perigosos. Os resultados demonstraram que o desempenho do BERT é superior a outros modelos e pode ser eficazmente utilizado para a tarefa de deteção de Eventos Perigosos. Salienta-se que a abordagem de divisão dos conjuntos de dados aumentou significativamente o desempenho dos modelos. Existem diversos métodos propostos para a deteção de eventos. A deteção destes eventos (ED) são maioritariamente classificados na categoria de supervisonado e não supervisionados, como demonstrado nos metódos supervisionados, estão incluidos support vector machine (SVM), Conditional random field (CRF), Decision tree (DT), Naive Bayes (NB), entre outros. Enquanto a categoria de não supervisionados inclui Query-based, Statisticalbased, Probabilistic-based, Clustering-based e Graph-based. Estas são as duas abordagens em uso na deteção de eventos e são denonimados de document-pivot and feature-pivot. A diferença entre estas abordagens é na sua maioria a clustering approach, a forma como os documentos são utilizados para caracterizar vetores, e a similaridade métrica utilizada para identificar se dois documentos correspondem ao mesmo evento ou não. Além da deteção de eventos, a previsão de eventos é um problema importante mas complicado que engloba diversas dimensões. Muitos destes eventos são difíceis de prever antes de se tornarem visíveis e ocorrerem. Como um exemplo, é impossível antecipar catástrofes naturais, sendo apenas detetáveis após o seu acontecimento. Existe um número limitado de recursos em ternos de conjuntos de dados de eventos. ACE 2005, MAVEN, EVIN são alguns dos exemplos de conjuntos de dados disponíveis para a deteção de evnetos. Os trabalhos recentes demonstraram que os Transformer-based pre-trained models (PTMs) são capazes de alcançar desempenho de última geração em várias tarefas de NLP. Estes modelos são pré-treinados em grandes quantidades de texto. Aprendem incorporações para as palavras da língua ou representações de vetores de modo a que as palavras que se relacionem se agrupen no espaço vectorial. Um total de três transformadores diferentes, nomeadamente BERT, RoBERTa, e XLNet, será utilizado para conduzir a experiência e tirar a conclusão através da comparação destes modelos. Os modelos baseados em transformação (Transformer-based) estão em total sintonia utilizando uma divisão de 70,30 dos conjuntos de dados para fins de formação e teste/validação. A sintonização do hiperparâmetro inclui 10 epochs, 16 batch size, e o optimizador AdamW com taxa de aprendizagem 2e-5 para BERT e RoBERTa e 3e-5 para XLNet. Para eventos perigosos, o BERT fornece 60%, o RoBERTa 59 enquanto a XLNet fornece apenas 54% de precisão geral. Para as outras experiências de configuração de eventos de alto nível, o BERT e a XLNet dão 71% e 70% de desempenho com RoBERTa em relação aos outros modelos com 74% de precisão. Enquanto para o DE baseado em acções, DE baseado em cenários, e DE baseado em sentimentos, o BERT dá 62%, 85%, e 81% respetivamente; RoBERTa com 61%, 83%, e 71%; a XLNet com 52%, 81%, e 77% de precisão. Existe a necessidade de clarificar a ambiguidade entre os diferentes trabalhos que abordam problemas similares utilizando termos diferentes. A ideia proposta de referir acontecimentos especifícos como eventos perigosos torna mais fácil a abordagem do problema em questão. No entanto, a escassez de conjunto de dados de eventos limita o desempenho dos modelos e o progresso na deteção das tarefas. A disponibilidade de uma maior quantidade de informação relacionada com eventos perigosos pode melhorar o desempenho do modelo existente. É evidente que o uso de modelos de aprendizagem profunda, tais como como BERT, RoBERTa, e XLNet, pode ajudar a detetar e classificar eventos perigosos de forma eficiente. Tem sido evidente que a utilização de modelos de aprendizagem profunda, tais como BERT, RoBERTa, e XLNet, pode ajudar a detetar e classificar eventos perigosos de forma eficiente. Em geral, o BERT tem um desempenho superior ao do RoBERTa e XLNet na detecção de eventos perigosos. É igualmente importante rastrear os eventos após a sua detecção. Por conseguinte, para trabalhos futuros, propõe-se a implementação das técnicas que lidam com o espaço e o tempo, a fim de monitorizar a sua emergência com o tempo

Sentiment and Authority Analysis in Conversational Content

This paper deals with mining conversational content from the social media. It focused on two issues: opinion and emotion classification and identification of authoritative reviewers. The paper also describes applications representing the results obtained in the given areas. Authority identification can be used by organizations to search for experts in their specific areas to employ them. The opinion and emotion analysis can be useful for providing decision-making support