Vasco Rodrigues

The amount of information accessible to businesses grows as more individuals get access to the internet. This information can be used by businesses to make corporate decisions that can highly affect them. E-commerce is the result of businesses starting to export their operations to the internet as a result of taking advantage of this reality. To help a footwear e-commerce platform keep up with the current demands of the market in terms of accessibility and customer service, a chatbot was… Exibir mais

The amount of information accessible to businesses grows as more individuals get access to the internet. This information can be used by businesses to make corporate decisions that can highly affect them. E-commerce is the result of businesses starting to export their operations to the internet as a result of taking advantage of this reality. To help a footwear e-commerce platform keep up with the current demands of the market in terms of accessibility and customer service, a chatbot was created to help on this request. To build this chatbot, datasets were created leading to Intent Classification and Named-Entity Recognition pre-trained models, utilizing Bert-Large, getting fine-tuned with those datasets. These models achieved a F1-score of 0.90 and 0.88 respectively in their tasks. The Sentiment Analysis functionality of TextBlob was also utilized to help the chatbot comprehend user’s text polarity to reply appropriately. Exibir menos

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… Exibir mais

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. Exibir menos

Decision making is an important part of our lives, especially in the context of an organization where decisions affect their business, and in this modern era, it is increasingly important to make the best decisions and increasingly difficult to get people together to make said decisions. Because of this, the importance of Group Decision Support Systems keeps growing, especially those that are web-based since they allow a connection between people in different corners of the world. However… Exibir mais

Decision making is an important part of our lives, especially in the context of an organization where decisions affect their business, and in this modern era, it is increasingly important to make the best decisions and increasingly difficult to get people together to make said decisions. Because of this, the importance of Group Decision Support Systems keeps growing, especially those that are web-based since they allow a connection between people in different corners of the world. However, there isn’t much in terms of systems that can take online text-based discussions and use them to help a group of people reach a decision. This works addresses one of the aspects of this issue, that being the lack of annotated datasets that can provide a source of information to help in the creation of said systems. For this purpose, this work presents a methodology to be applied to unstructured text-based discussions found on the social web, to extract from them important information and organize it. In addition, a practical case study of this methodology is described, using Baseball domain discussions from Reddit as this case’s unstructured data. We concluded that the created methodology allows the structuring of different aspects of a given social web discussion, especially in Reddit, and could be applied to discussions found on several existing domains. Exibir menos