Supporting argumentation dialogues in group decision support systems: an approach based on dynamic clustering

Group decision support systems (GDSSs) have been widely studied over the recent decades. The Web-based group decision support systems appeared to support the group decision-making process by creating the conditions for it to be effective, allowing the management and participation in the process to be carried out from any place and at any time. In GDSS, argumentation is ideal, since it makes it easier to use justifications and explanations in interactions between decision-makers so they can sustain their opinions. Aspect-based sentiment analysis (ABSA) intends to classify opinions at the aspect level and identify the elements of an opinion. Intelligent reports for GDSS provide decision makers with accurate information about each decision-making round. Applying ABSA techniques to group decision making context results in the automatic identification of alternatives and criteria, for instance. This automatic identification is essential to reduce the time decision makers take to step themselves up on group decision support systems and to offer them various insights and knowledge on the discussion they are participating in. In this work, we propose and implement a methodology that uses an unsupervised technique and clustering to group arguments on topics around a specific alternative, for example, or a discussion comparing two alternatives. We experimented with several combinations of word embedding, dimensionality reduction techniques, and different clustering algorithms to achieve the best approach. The best method consisted of applying the KMeans++ clustering technique, using SBERT as a word embedder with UMAP dimensionality reduction. These experiments achieved a silhouette score of 0.63 with eight clusters on the baseball dataset, which wielded good cluster results based on their manual review and word clouds. We obtained a silhouette score of 0.59 with 16 clusters on the car brand dataset, which we used as an approach validation dataset. With the results of this work, intelligent reports for GDSS become even more helpful, since they can dynamically organize the conversations taking place by grouping them on the arguments used.This research was funded by National Funds through the Portuguese FCT-Fundacao para a Ciencia e a Tecnologia under the R&D Units Project Scope UIDB/00319/2020, UIDB/00760/2020, UIDP/00760/2020, and by the Luis Conceicao Ph.D. Grant with the reference SFRH/BD/137150/2018

Flexible document organization: comparing fuzzy and possibilistic approaches

System flexibility means the ability of a system to manage imprecise and/or uncertain information. A lot of commercially available Information Retrieval Systems (IRS) address this issue at the level of query formulation. Another way to make the flexibility of an IRS possible is by means of the flexible organization of documents. Such organization can be carried out using clustering algorithms by which documents can be automatically organized in multiple clusters simultaneously. Fuzzy and possibilistic clustering algorithms are examples of methods by which documents can belong to more than one cluster simultaneously with different membership degrees. The interpretation of these membership degrees can be used to quantify the compatibility of a document with a particular topic. The topics are represented by clusters and the clusters are identified by one or more descriptors extracted by a proposed method. We aim to investigate if the performance of each clustering algorithm can affect the extraction of meaningful overlapping cluster descriptors. Experiments were carried using well-known collections of documents and the predictive power of the descriptors extracted from both fuzzy and possibilistic document clustering was evaluated. The results prove that descriptors extracted after both fuzzy and possibilistic clustering are effective and can improve the flexible organization of documents.CAPES (Coordination for the Improvement of Higher Level Personnel) (PDSE grant 5983-11-8)FAPESP (Sao Paulo Research Foundation) (grant 2011/19850-9

Aplicação de técnicas de Clustering ao contexto da Tomada de Decisão em Grupo

Nowadays, decisions made by executives and managers are primarily made in a group. Therefore, group decision-making is a process where a group of people called participants work together to analyze a set of variables, considering and evaluating a set of alternatives to select one or more solutions. There are many problems associated with group decision-making, namely when the participants cannot meet for any reason, ranging from schedule incompatibility to being in different countries with different time zones. To support this process, Group Decision Support Systems (GDSS) evolved to what today we call web-based GDSS. In GDSS, argumentation is ideal since it makes it easier to use justifications and explanations in interactions between decision-makers so they can sustain their opinions. Aspect Based Sentiment Analysis (ABSA) is a subfield of Argument Mining closely related to Natural Language Processing. It intends to classify opinions at the aspect level and identify the elements of an opinion. Applying ABSA techniques to Group Decision Making Context results in the automatic identification of alternatives and criteria, for example. This automatic identification is essential to reduce the time decision-makers take to step themselves up on Group Decision Support Systems and offer them various insights and knowledge on the discussion they are participants. One of these insights can be arguments getting used by the decision-makers about an alternative. Therefore, this dissertation proposes a methodology that uses an unsupervised technique, Clustering, and aims to segment the participants of a discussion based on arguments used so it can produce knowledge from the current information in the GDSS. This methodology can be hosted in a web service that follows a micro-service architecture and utilizes Data Preprocessing and Intra-sentence Segmentation in addition to Clustering to achieve the objectives of the dissertation. Word Embedding is needed when we apply clustering techniques to natural language text to transform the natural language text into vectors usable by the clustering techniques. In addition to Word Embedding, Dimensionality Reduction techniques were tested to improve the results. Maintaining the same Preprocessing steps and varying the chosen Clustering techniques, Word Embedders, and Dimensionality Reduction techniques came up with the best approach. This approach consisted of the KMeans++ clustering technique, using SBERT as the word embedder with UMAP dimensionality reduction, reducing the number of dimensions to 2. This experiment achieved a Silhouette Score of 0.63 with 8 clusters on the baseball dataset, which wielded good cluster results based on their manual review and Wordclouds. The same approach obtained a Silhouette Score of 0.59 with 16 clusters on the car brand dataset, which we used as an approach validation dataset.Atualmente, as decisões tomadas por gestores e executivos são maioritariamente realizadas em grupo. Sendo assim, a tomada de decisão em grupo é um processo no qual um grupo de pessoas denominadas de participantes, atuam em conjunto, analisando um conjunto de variáveis, considerando e avaliando um conjunto de alternativas com o objetivo de selecionar uma ou mais soluções. Existem muitos problemas associados ao processo de tomada de decisão, principalmente quando os participantes não têm possibilidades de se reunirem (Exs.: Os participantes encontramse em diferentes locais, os países onde estão têm fusos horários diferentes, incompatibilidades de agenda, etc.). Para suportar este processo de tomada de decisão, os Sistemas de Apoio à Tomada de Decisão em Grupo (SADG) evoluíram para o que hoje se chamam de Sistemas de Apoio à Tomada de Decisão em Grupo baseados na Web. Num SADG, argumentação é ideal pois facilita a utilização de justificações e explicações nas interações entre decisores para que possam suster as suas opiniões. Aspect Based Sentiment Analysis (ABSA) é uma área de Argument Mining correlacionada com o Processamento de Linguagem Natural. Esta área pretende classificar opiniões ao nível do aspeto da frase e identificar os elementos de uma opinião. Aplicando técnicas de ABSA à Tomada de Decisão em Grupo resulta na identificação automática de alternativas e critérios por exemplo. Esta identificação automática é essencial para reduzir o tempo que os decisores gastam a customizarem-se no SADG e oferece aos mesmos conhecimento e entendimentos sobre a discussão ao qual participam. Um destes entendimentos pode ser os argumentos a serem usados pelos decisores sobre uma alternativa. Assim, esta dissertação propõe uma metodologia que utiliza uma técnica não-supervisionada, Clustering, com o objetivo de segmentar os participantes de uma discussão com base nos argumentos usados pelos mesmos de modo a produzir conhecimento com a informação atual no SADG. Esta metodologia pode ser colocada num serviço web que segue a arquitetura micro serviços e utiliza Preprocessamento de Dados e Segmentação Intra Frase em conjunto com o Clustering para atingir os objetivos desta dissertação. Word Embedding também é necessário para aplicar técnicas de Clustering a texto em linguagem natural para transformar o texto em vetores que possam ser usados pelas técnicas de Clustering. Também Técnicas de Redução de Dimensionalidade também foram testadas de modo a melhorar os resultados. Mantendo os passos de Preprocessamento e variando as técnicas de Clustering, Word Embedder e as técnicas de Redução de Dimensionalidade de modo a encontrar a melhor abordagem. Essa abordagem consiste na utilização da técnica de Clustering KMeans++ com o SBERT como Word Embedder e UMAP como a técnica de redução de dimensionalidade, reduzindo as dimensões iniciais para duas. Esta experiência obteve um Silhouette Score de 0.63 com 8 clusters no dataset de baseball, que resultou em bons resultados de cluster com base na sua revisão manual e visualização dos WordClouds. A mesma abordagem obteve um Silhouette Score de 0.59 com 16 clusters no dataset das marcas de carros, ao qual usamos esse dataset com validação de abordagem

Stock market uncertainty determination with news headlines : a digital twin approach

Published online: 13 December 2023We present a novel digital twin model that implements advanced artificial intelligence techniques to robustly link news and stock market uncertainty. On the basis of central results in financial economics, our model efficiently identifies, quantifies, and forecasts the uncertainty encapsulated in the news by mirroring the human mind’s information processing mechanisms. After obtaining full statistical descriptions of the timeline and contextual patterns of the appearances of specific words, the applied data mining techniques lead to the definition of regions of homogeneous knowledge. The absence of a clear assignment of informative elements to specific knowledge regions is regarded as uncertainty, which is then measured and quantified using Shannon Entropy. As compared with standard models, the empirical analyses demonstrate the effectiveness of this approach in anticipating stock market uncertainty, thus showcasing a meaningful integration of natural language processing, artificial intelligence, and information theory to comprehend the perception of uncertainty encapsulated in the news by market agents and its subsequent impact on stock markets

Argumentation dialogues in web-based GDSS: an approach using machine learning techniques

Tese de doutoramento em InformaticsA tomada de decisão está presente no dia a dia de qualquer pessoa, mesmo que muitas vezes ela não tenha consciência disso. As decisões podem estar relacionadas com problemas quotidianos, ou podem estar relacionadas com questões mais complexas, como é o caso das questões organizacionais. Normalmente, no contexto organizacional, as decisões são tomadas em grupo. Os Sistemas de Apoio à Decisão em Grupo têm sido estudados ao longo das últimas décadas com o objetivo de melhorar o apoio prestado aos decisores nas mais diversas situações e/ou problemas a resolver. Existem duas abordagens principais à implementação de Sistemas de Apoio à Decisão em Grupo: a abordagem clássica, baseada na agregação matemática das preferências dos diferentes elementos do grupo e as abordagens baseadas na negociação automática (e.g. Teoria dos Jogos, Argumentação, entre outras). Os atuais Sistemas de Apoio à Decisão em Grupo baseados em argumentação podem gerar uma enorme quantidade de dados. O objetivo deste trabalho de investigação é estudar e desenvolver modelos utilizando técnicas de aprendizagem automática para extrair conhecimento dos diálogos argumentativos realizados pelos decisores, mais concretamente, pretende-se criar modelos para analisar, classificar e processar esses dados, potencializando a geração de novo conhecimento que será utilizado tanto por agentes inteligentes, como por decisiores reais. Promovendo desta forma a obtenção de consenso entre os membros do grupo. Com base no estudo da literatura e nos desafios em aberto neste domínio, formulou-se a seguinte hipótese de investigação - É possível usar técnicas de aprendizagem automática para apoiar diálogos argumentativos em Sistemas de Apoio à Decisão em Grupo baseados na web. No âmbito dos trabalhos desenvolvidos, foram aplicados algoritmos de classificação supervisionados a um conjunto de dados contendo argumentos extraídos de debates online, criando um classificador de frases argumentativas que pode classificar automaticamente (A favor/Contra) frases argumentativas trocadas no contexto da tomada de decisão. Foi desenvolvido um modelo de clustering dinâmico para organizar as conversas com base nos argumentos utilizados. Além disso, foi proposto um Sistema de Apoio à Decisão em Grupo baseado na web que possibilita apoiar grupos de decisores independentemente de sua localização geográfica. O sistema permite a criação de problemas multicritério e a configuração das preferências, intenções e interesses de cada decisor. Este sistema de apoio à decisão baseado na web inclui os dashboards de relatórios inteligentes que são gerados através dos resultados dos trabalhos alcançados pelos modelos anteriores já referidos. A concretização de cada um dos objetivos permitiu validar as questões de investigação identificadas e assim responder positivamente à hipótese definida.Decision-making is present in anyone’s daily life, even if they are often unaware of it. Decisions can be related to everyday problems, or they can be related to more complex issues, such as organizational issues. Normally, in the organizational context, decisions are made in groups. Group Decision Support Systems have been studied over the past decades with the aim of improving the support provided to decision-makers in the most diverse situations and/or problems to be solved. There are two main approaches to implementing Group Decision Support Systems: the classical approach, based on the mathematical aggregation of the preferences of the different elements of the group, and the approaches based on automatic negotiation (e.g. Game Theory, Argumentation, among others). Current argumentation-based Group Decision Support Systems can generate an enormous amount of data. The objective of this research work is to study and develop models using automatic learning techniques to extract knowledge from argumentative dialogues carried out by decision-makers, more specifically, it is intended to create models to analyze, classify and process these data, enhancing the generation of new knowledge that will be used both by intelligent agents and by real decision-makers. Promoting in this way the achievement of consensus among the members of the group. Based on the literature study and the open challenges in this domain, the following research hypothesis was formulated - It is possible to use machine learning techniques to support argumentative dialogues in web-based Group Decision Support Systems. As part of the work developed, supervised classification algorithms were applied to a data set containing arguments extracted from online debates, creating an argumentative sentence classifier that can automatically classify (For/Against) argumentative sentences exchanged in the context of decision-making. A dynamic clustering model was developed to organize conversations based on the arguments used. In addition, a web-based Group Decision Support System was proposed that makes it possible to support groups of decision-makers regardless of their geographic location. The system allows the creation of multicriteria problems and the configuration of preferences, intentions, and interests of each decision-maker. This web-based decision support system includes dashboards of intelligent reports that are generated through the results of the work achieved by the previous models already mentioned. The achievement of each objective allowed validation of the identified research questions and thus responded positively to the defined hypothesis.I also thank to Fundação para a Ciência e a Tecnologia, for the Ph.D. grant funding with the reference: SFRH/BD/137150/2018

Context-awareness for adaptive information retrieval systems

Philosophiae Doctor - PhDThis research study investigates optimization of IRS to individual information needs in order of relevance. The research addressed development of algorithms that optimize the ranking of documents retrieved from IRS. In this thesis, we present two aspects of context-awareness in IR. Firstly, the design of context of information. The context of a query determines retrieved information relevance. Thus, executing the same query in diverse contexts often leads to diverse result rankings. Secondly, the relevant context aspects should be incorporated in a way that supports the knowledge domain representing users’ interests. In this thesis, the use of evolutionary algorithms is incorporated to improve the effectiveness of IRS. A context-based information retrieval system is developed whose retrieval effectiveness is evaluated using precision and recall metrics. The results demonstrate how to use attributes from user interaction behaviour to improve the IR effectivenes