Sentiment analysis in retail: the case of Parfois facebook page

The way that consumers are interacting with brands is changing, and in Retail it is no different. With the growth of internet usage and with all the social networks that we interact with, social media is gaining more and more relevance and importance. This research extracted 1.845 posts, 8.256 comments and more than 500.000 reactions from Parfois Facebook page. The comments were translated to English due to having comments made in several different languages, modelled and finally made the sentiment analysis. This analysis was made concerning the post dates, the reasons of the post and the products associated in the post. It was used decision tree algorithms to predict sentiments, so it can be predicted the sentiment when making a new post. With the Sentiment Analysis from Social Media, Parfois can gain understanding about their own brand, from the marketing department through to the buying or even design departments. Using Social Media analysis together with Business Intelligence, can help Parfois decision makers gain competitive advantage regarding their competitors or even improve their products.A maneira como os consumidores interagem com as marcas está a mudar, e no retalho não é diferente. Com o aumento do uso da internet e com todas as redes sociais que interagimos, as redes sociais ganham mais relevância e importância. Esta pesquisa extraiu 1.845 posts, 8.256 comentários e mais de 500.000 reações da página de Facebook da Parfois. Os comentários foram traduzidos para o inglês devido ao fato de haver comentários feitos em várias línguas diferentes, modelados e finalmente feita a análise de sentimentos. Esta análise foi feita em relação às datas das publicações, os motivos do post, os produtos associados ao post. Foram utilizados algoritmos de árvores de decisão para prever sentimentos para que se possa prever o sentimento ao fazer um novo post. Com a analise de sentimentos das redes sociais, a Parfois pode entender melhor a sua própria marca, desde o departamento de marketing até ao departamento de compras ou mesmo o departamento de design. Usar a análise de sentimentos das redes sociais junto com o Business Intelligence organizacional, pode ajudar os decisores da Parfois a ganhar vantagem competitiva em relação aos concorrentes ou mesmo a melhorar seus produtos

Simulating Land Use Land Cover Change Using Data Mining and Machine Learning Algorithms

The objectives of this dissertation are to: (1) review the breadth and depth of land use land cover (LUCC) issues that are being addressed by the land change science community by discussing how an existing model, Purdue\u27s Land Transformation Model (LTM), has been used to better understand these very important issues; (2) summarize the current state-of-the-art in LUCC modeling in an attempt to provide a context for the advances in LUCC modeling presented here; (3) use a variety of statistical, data mining and machine learning algorithms to model single LUCC transitions in diverse regions of the world (e.g. United States and Africa) in order to determine which tools are most effective in modeling common LUCC patterns that are nonlinear; (4) develop new techniques for modeling multiple class (MC) transitions at the same time using existing LUCC models as these models are rare and in great demand; (5) reconfigure the existing LTM for urban growth boundary (UGB) simulation because UGB modeling has been ignored by the LUCC modeling community, and (6) compare two rule based models for urban growth boundary simulation for use in UGB land use planning. The review of LTM applications during the last decade indicates that a model like the LTM has addressed a majority of land change science issues although it has not explicitly been used to study terrestrial biodiversity issues. The review of the existing LUCC models indicates that there is no unique typology to differentiate between LUCC model structures and no models exist for UGB. Simulations designed to compare multiple models show that ANN-based LTM results are similar to Multivariate Adaptive Regression Spline (MARS)-based models and both ANN and MARS-based models outperform Classification and Regression Tree (CART)-based models for modeling single LULC transition; however, for modeling MC, an ANN-based LTM-MC is similar in goodness of fit to CART and both models outperform MARS in different regions of the world. In simulations across three regions (two in United States and one in Africa), the LTM had better goodness of fit measures while the outcome of CART and MARS were more interpretable and understandable than the ANN-based LTM. Modeling MC LUCC require the examination of several class separation rules and is thus more complicated than single LULC transition modeling; more research is clearly needed in this area. One of the greatest challenges identified with MC modeling is evaluating error distributions and map accuracies for multiple classes. A modified ANN-based LTM and a simple rule based UGBM outperformed a null model in all cardinal directions. For UGBM model to be useful for planning, other factors need to be considered including a separate routine that would determine urban quantity over time

European tourist perspective on destination satisfaction: a business analytics approach

For many years that tourism information has been collected and stored, allowing increased interest in the data mining (DM) areas. This leads to a need of research and discovery of new patterns to develop automated procedures to improve the tourism knowledge management. The relationship between the tourist characteristics and preferences and the tourist satisfaction was never studied in order to provide useful knowledge to the tourism industry. Therefore, there was the need to investigate the explanatory factors of the tourist satisfaction with the destination to allow the tourism companies to define the correct assumptions about a certain travel. This dissertation used the data from Flash Eurobarometer 414 “Preferences of Europeans towards tourism 2015” with data from the 28 countries of the European Union (EU). A predictive model was obtained for the tourist satisfaction, through the discovery of existing patterns in the process of the tourist travel, using DM techniques on the data referred above. The definition of an explanatory model allowed to obtain useful knowledge for tourism agencies, enabling the development of marketing strategies according to the tourist profile and ensuring development of promotional messages for products and experiences, ensuring that correct assumptions are made about their customers.Desde há muito tempo que é recolhida e armazenada informação sobre turismo, permitindo captar o interesse das áreas de data mining (DM). Consequentemente, surgiu a necessidade de pesquisa e descoberta de novos padrões para desenvolver procedimentos automatizados, de forma a melhorar a gestão deste tipo de informação. A relação entre as características do turista, as suas preferências e a satisfação nunca foram estudadas extensivamente de forma a criar conhecimento útil para a indústria do turismo. Desta forma, havia a necessidade de investigar e estudar os fatores explicativos da satisfação do turista com o destino, para que seja possível às empresas de turismo traçar o perfil de turista adequado e transmitir as campanhas de marketing de forma assertiva e eficiente. Nesta dissertação foram utilizados os dados do Flash Eurobarometer 414 “Preferences of Europeans towards tourism 2015”, que contém dados dos 28 países da União Europeia. Através da descoberta de padrões existentes no processo de viagem do turista, utilizando técnicas de DM sobre os dados acima referidos, foi possível obter um modelo preditivo para a satisfação do turista. A definição de um modelo explicativo permitiu obter conhecimento útil para as empresas de turismo, facilitando o desenvolvimento de estratégias de marketing de acordo com o perfil do turista e de mensagens promocionais para produtos e experiências, garantindo que são definidos pressupostos adequados para os seus clientes

Gestão de resultados como preditor de insolvências: evidência nas empresas portuguesas

As demonstrações financeiras reportadas pelas empresas nem sempre transparecem a verdadeira “saúde” das mesmas, visto recorrerem a mecanismos de Earnings Management com o intuito de alterar os resultados para os desejados nesse momento. Assim sendo, a insustentabilidade destas práticas de gestão de resultados, em virtude da obtenção de benefícios, pode originar situações de insolvência inesperadas. A presente dissertação pretende avaliar o impacto da existência de Earnings Management na previsão das insolvências, alvo de diversas investigações científicas ao longo dos últimos anos. Para tal, procedemos à análise de informações financeiras, compreendidas entre 2010 e 2018, para 17.665 empresas portuguesas, retiradas da Base de Dados Bureau van Djik’s Amadeus. Adicionalmente, foram calculados quatro modelos que se debruçam sobre o estudo da gestão de resultados assentes em accruals, o Original Jones, o Modified Jones, o Cash-flow Jones e Performance Jones, assim como quatro variáveis de controlo consideradas relevantes. Deste modo, suportámos esta investigação em testes aos modelos estatísticos de previsão existentes na literatura, que visaram o Modelo Logístico e posteriormente o recurso às Árvores de Decisão, através dos softwares SPSS Statistics e SPSS Modeler. Por fim, os resultados obtidos indiciaram a existência de uma relação entre os Earnings Management (em conjunto com as variáveis de controlo) e as insolvências das empresas, prevendo uma percentagem de exemplos corretamente classificados de 96% (para empresas insolventes), comprovada para uma amostra com uma abrangência mais alargada, quando comparada com os restantes estudos presentes na literatura.The financial statements reported by the companies do not always show their true "health", since they resort to Earnings Management mechanisms in order to change the results to those desired at that time. Therefore, the unsustainability of these Earnings Management practices, linked with the obtaining of benefits, can emerge in unexpected situations of insolvency. This dissertation aims to evaluate the impact of the existence of Earnings Management in the insolvency forecasts, target of several scientific investigations over the last years. To this end, we have analyzed financial information, between 2010 and 2018, for 17.665 portuguese companies, through the Bureau van Djik's Amadeus Database. In addition, we calculated four models that focus on the study of Earnings Management based on accruals, Original Jones, Modified Jones, Cash-flow Jones and Performance Jones, as well as four control variables considered relevant. Consequently, we supported this research in tests of statistical prediction models in the literature, which targeted the logistic model and later the use of Decision Trees, through the software SPSS Statistics and SPSS Modeler. Finally, the results obtained indicated the existence of a relationship between Earnings Management (along with the control variables) and company insolvencies, achieving an accuracy of 96% (for insolvent companies), proven for a sample with a wider scope, when compared with other studies present in the literature