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Projeto de investigação
Information Sciences, Technologies and Architecture Research Center
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Publicações
Electronic Word-of-Mouth and Tourist Satisfaction in Rural Tourism in Schist Villages
Publication . Santos, Marta; Rita, Paulo; Moro, Sérgio; Alturas, Bráulio; NOVA Information Management School (NOVA IMS); Information Management Research Center (MagIC) - NOVA Information Management School
Consumers' decision-making processes and the way they purchase their products and services have been evolving over the years due to the influence of information technologies. Tourists are increasingly making their decisions based on online reviews made by other users, which contain descriptive comments and/or a rating system, leveraging electronic word-of-mouth (eWOM). This study aims to understand the variation of the eWOM in rural tourism as well as unveil the main characteristics that influence the satisfaction and the interest of the consumers. To that end, the content of the comments and quantitative classification of Portuguese schist villages' lodgings on the platforms of TripAdvisor and Facebook were studied using both sentiment polarity and frequency analysis. The results show that eWOM has increased in rural tourism and that the satisfaction of tourists are more influenced by the friendliness of the hosts, the variety and good breakfast or Portuguese cuisine, and the service provided.
Data Driven Spatiotemporal Analysis of e-Cargo Bike Network in Lisbon and Its Expansion
Publication . Gil, Bruno; Albuquerque, Vitoria; Dias, Miguel Sales; Abranches, Rui; Ogando, Manuel; Information Management Research Center (MagIC) - NOVA Information Management School; NOVA Information Management School (NOVA IMS)
The adoption of more environmentally friendly and sustainable fleets for last-mile parcel delivery within large urban centers, such as e-cargo bikes, has gained the interest of the community. The logistics infrastructure network, had to adapt to the requirements of this new type of fleet, and micro-hubs and nano-hubs emerged. In this paper we tackle spatiotemporal characterization of e-cargo bike fleet behavior, by conducting a data centered case study where we explore data from Yoob, a last mile delivery e-cargo bike logistics startup that operates in the Lisbon area and outskirts. We also address the identification of potential expansion locations to the establishment of new hubs. Our data was collected during a 4-month period (January to April 2022). By adopting state-of-the-art data science and machine learning techniques, and following the CRIPS-DM data mining method, our innovative approach discovered five clusters that are able to characterize the Yoob fleet, with variations in distances traveled, times, transported volumes and speeds. In the perspective of expanding Yoob’s e-cargo bike network, three new locations in Lisbon were signaled for potential new hub installation. To the authors knowledge this is the first study of this kind carried in Portugal, bringing new insights in the field of last-mile logistics.
Unlocking the power of Twitter communities for startups
Publication . Peixoto, Ana Rita; Almeida, Ana de; António, Nuno; Batista, Fernando; Ribeiro, Ricardo; Cardoso, Elsa; Information Management Research Center (MagIC) - NOVA Information Management School; NOVA Information Management School (NOVA IMS); Springer Nature
Social media platforms offer cost-effective digital marketing opportunities to monitor the market, create user communities, and spread positive opinions. They allow companies with fewer budgets, like startups, to achieve their goals and grow. In fact, studies found that startups with active engagement on those platforms have a higher chance of succeeding and receiving funding from venture capitalists. Our study explores how startups utilize social media platforms to foster social communities. We also aim to characterize the individuals within these communities. The findings from this study underscore the importance of social media for startups. We used network analysis and visualization techniques to investigate the communities of Portuguese IT startups through their Twitter data. For that, a social digraph has been created, and its visualization shows that each startup created a community with a degree of intersecting followers and following users. We characterized those users using user node-level measures. The results indicate that users who are followed by or follow Portuguese IT startups are of these types: “Person”, “Company,” “Blog,” “Venture Capital/Investor,” “IT Event,” “Incubators/Accelerators,” “Startup,” and “University.” Furthermore, startups follow users who post high volumes of tweets and have high popularity levels, while those who follow them have low activity and are unpopular. The attained results reveal the power of Twitter communities and offer essential insights for startups to consider when building their social media strategies. Lastly, this study proposes a methodological process for social media community analysis on platforms like Twitter.
Neural Hierarchical Interpolation Time Series (NHITS) for Reservoir Level Multi-Horizon Forecasting in Hydroelectric Power Plants
Publication . Stefenon, Stefano Frizzo; Seman, Laio Oriel; Yamaguchi, Cristina Keiko; Coelho, Leandro Dos Santos; Mariani, Viviana Cocco; Matos-Carvalho, Joao Pedro; Leithardt, Valderi Reis Quietinho; UNINOVA-Instituto de Desenvolvimento de Novas Tecnologias; Institute of Electrical and Electronics Engineers (IEEE)
Energy planning in systems heavily influenced by hydroelectric power is based on assessing the availability of water in the future. In Brazil, based on the soil moisture active passive, the National Electricity System Operator defines electricity dispatch concerning a stochastic optimization problem. Currently, machine learning models are an alternative for improving forecasts, and could be a promising solution for predicting reservoir levels at hydroelectric dams. In this paper, neural hierarchical interpolation for time series (NHITS) is applied to improve forecasts and thus help decision-making in the management of electric power systems. The NHITS model achieved a root mean square error of 4.64×10-4 for a 1-hour forecast horizon, and 1.03×10-3for a 10-hour forecast horizon, being superior to multilayer perceptron (MLP) neural network, long short-term memory (LSTM), convolutional neural network with long short-term memory (CNN-LSTM), recurrent neural network (RNN), Dilated RNN, temporal convolutional neural (TCN), neural basis expansion analysis for interpretable time series forecasting (N-BEATS), and deep non-parametric time series forecaster (DeepNPTS) deep learning approaches.
A Proposed Intelligent Model with Optimization Algorithm for Clustering Energy Consumption in Public Buildings
Publication . Abdelaziz, Ahmed; Santos, Vítor; Dias, Miguel Sales; NOVA Information Management School (NOVA IMS); Information Management Research Center (MagIC) - NOVA Information Management School; Escuela de Historia, Universidad Nacional de Salta
Recently, intelligent applications gained a significant role in the energy management of public buildings due to their ability to enhance energy consumption performance. Energy management of these buildings represents a big challenge due to their unexpected energy consumption characteristics and the deficiency of design guidelines for energy efficiency and sustainability solutions. Therefore, an analysis of energy consumption patterns in public buildings becomes necessary. This reveals the significance of understanding and classifying energy consumption patterns in these buildings. This study seeks to find the optimal intelligent technique for classifying energy consumption of public buildings into levels (e.g., low, medium, and high), find the critical factors that influence energy consumption, and finally, find the scientific rules (If-Then rules) to help decision-makers for determining the energy consumption level in each building. To achieve the objectives of this study, correlation coefficient analysis was used to determine critical factors that influence on energy consumption of public buildings; two intelligent models were used to determine the number of clusters of energy consumption patterns which are Self Organizing Map (SOM) and Batch-SOM based on Principal Component Analysis (PCA). SOM outperforms Batch-SOM in terms of quantization error. The quantization error of SOM and Batch-SOM is 8.97 and 9.24, respectively. K-means with a genetic algorithm were used to predict cluster levels in each building. By analyzing cluster levels, If-Then rules have been extracted, so needs that decision-makers determine the most energyconsuming buildings. In addition, this study helps decisionmakers in the energy field to rationalize the consumption of occupants of public buildings in the times that consume the most energy and change energy suppliers to those buildings.
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Entidade financiadora
Fundação para a Ciência e a Tecnologia
Programa de financiamento
6817 - DCRRNI ID
Número da atribuição
UIDB/04466/2020