O Mestrado em Sistemas Integrados de Apoio à Decisão (MSIAD) foi criado em 2005 com o objetivo de formar profissionais especialistas em Business Intelligence, aptos a gerir, especificar, implementar e usar com sucesso sistemas que apoiem os processos de decisão, devidamente integrados na gestão da informação organizacional.
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Searching for associations between social media trending topics and organizations
João Pedro Sousa Henrique
Master in Integrated Business Intelligence Systems
December, 2020
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Tese em MSIAD, classificada com 18 valores
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Abstract
This work focuses on how micro and small companies can take advantage of trending topics for marketing campaigns. Trending topics are the most discussed topics at the moment on social media platforms, particularly on Twitter and Facebook. While the access to trending topics is free and available to everyone, marketing specialists and specific software are more expensive, therefore small companies do not have the budget to support those costs. The main goal is to search for associations between trending topics and companies on social media platforms and HotRivers prototype is designed to accomplish this. A solution that aims to be inexpensive, fast, and automated. Detailed analyses were conducted to reduced the time and maximize the resources available at the lowest price. The final user receives a list of the trending topics related to the target company. For HotRivers were tested different pre-processing text techniques, a method to select tweets called Centroid Strategy and three models, an embedding vectors approach with Doc2Vec model, a probabilistic model with Latent Dirichlet Allocation, and a classification task approach with a Convolutional Neural Network used on the final architecture. The Centroid Strategy is used on trending topics to avoid unwanted tweets. In the results stand out that trending topic Nike has an association with the company Nike and #World- PatientSafetyDay has an association with Portsmouth Hospitals University. HotRivers cannot produce a full marketing campaign but can point out to the direction to the next campaign.
Keywords: Trending Topics, Text Similarity, Text Classification, Associations.
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Energy Consumption Forecasting – A Proposed Framework
Hugo Miguel Nogueira Mende
A Dissertation presented in partial fulfilment of the requirements of
the Degree of Master in Integrated Business Intelligence Systems
September, 2020
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Tese em MSIAD, classificada com 19 valores
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Abstract
With the development of underdeveloped countries and the digitization of societies, energy consumption is expected to continue to show high growth in the coming decades. While there is still a strong focus on fossil fuels for energy generation, the implementation of energy policies is crucial to gradually shift to renewable sources and the consequent reduction in CO2 emissions. Buildings are currently the sector that consumes the most energy. To contribute for a better energy consumption efficiency, it was proposed a framework, to be applied to buildings or households, to allow users to know their energy consumption and the possibility to forecast it. Different data analysis techniques for time series were used to provide information to the user about their energy consumption as well as to validate important data characteristics, namely stationarity and the existence of seasonality, which can have an impact in the forecasting models. For the definition of the forecasting models, state of the art was done to identify used models for energy consumption forecasting, and three models were tested for both types of data, univariate and multivariate. For the univariate data, the tested models were SARIMA, Holt-Winters and LSTM as for the multivariate data, SARIMA with exogenous variables, Support Vector Regression and LSTM. After the first execution of each model, hyperparameter tuning was done to conclude on the improvement of the results and the robustness of the models for later application to the framework.
Keywords: energy consumption, forecasting, framework, data analysis
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Em caso de urgência envie e-mail diretamente ao director do mestrado, o professor João Carlos Ferreira.
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