Collaborative-demographic hybrid for financial: product recommendation

Internship Report presented as the partial requirement for obtaining a Master's degree in Data Science and Advanced AnalyticsDue to the increased availability of mature data mining and analysis technologies supporting CRM processes, several financial institutions are striving to leverage customer data and integrate insights regarding customer behaviour, needs, and preferences into their marketing approach. As decision support systems assisting marketing and commercial efforts, Recommender Systems applied to the financial domain have been gaining increased attention. This thesis studies a Collaborative- Demographic Hybrid Recommendation System, applied to the financial services sector, based on real data provided by a Portuguese private commercial bank. This work establishes a framework to support account managers’ advice on which financial product is most suitable for each of the bank’s corporate clients. The recommendation problem is further developed by conducting a performance comparison for both multi-output regression and multiclass classification prediction approaches. Experimental results indicate that multiclass architectures are better suited for the prediction task, outperforming alternative multi-output regression models on the evaluation metrics considered. Withal, multiclass Feed-Forward Neural Networks, combined with Recursive Feature Elimination, is identified as the topperforming algorithm, yielding a 10-fold cross-validated F1 Measure of 83.16%, and achieving corresponding values of Precision and Recall of 84.34%, and 85.29%, respectively. Overall, this study provides important contributions for positioning the bank’s commercial efforts around customers’ future requirements. By allowing for a better understanding of customers’ needs and preferences, the proposed Recommender allows for more personalized and targeted marketing contacts, leading to higher conversion rates, corporate profitability, and customer satisfaction and loyalty

Coronavirus Optimization Algorithm: A Bioinspired Metaheuristic Based on the COVID-19 Propagation Model

This study proposes a novel bioinspired metaheuristic simulating how the coronavirus spreads and infects healthy people. From a primary infected individual (patient zero), the coronavirus rapidly infects new victims, creating large populations of infected people who will either die or spread infection. Relevant terms such as reinfection probability, super-spreading rate, social distancing measures, or traveling rate are introduced into the model to simulate the coronavirus activity as accurately as possible. The infected population initially grows exponentially over time, but taking into consideration social isolation measures, the mortality rate, and number of recoveries, the infected population gradually decreases. The coronavirus optimization algorithm has two major advantages when compared with other similar strategies. First, the input parameters are already set according to the disease statistics, preventing researchers from initializing them with arbitrary values. Second, the approach has the ability to end after several iterations, without setting this value either. Furthermore, a parallel multivirus version is proposed, where several coronavirus strains evolve over time and explore wider search space areas in less iterations. Finally, the metaheuristic has been combined with deep learning models, to find optimal hyperparameters during the training phase. As application case, the problem of electricity load time series forecasting has been addressed, showing quite remarkable performance.Ministerio de Economía y Competitividad TIN2017-88209-C

Kaggle forecasting competitions: An overlooked learning opportunity

Competitions play an invaluable role in the field of forecasting, as exemplified through the recent M4 competition. The competition received attention from both academics and practitioners and sparked discussions around the representativeness of the data for business forecasting. Several competitions featuring real-life business forecasting tasks on the Kaggle platform has, however, been largely ignored by the academic community. We believe the learnings from these competitions have much to offer to the forecasting community and provide a review of the results from six Kaggle competitions. We find that most of the Kaggle datasets are characterized by higher intermittence and entropy than the M-competitions and that global ensemble models tend to outperform local single models. Furthermore, we find the strong performance of gradient boosted decision trees, increasing success of neural networks for forecasting, and a variety of techniques for adapting machine learning models to the forecasting task

Artificial Intelligence for Sustainability—A Systematic Review of Information Systems Literature

The booming adoption of Artificial Intelligence (AI) likewise poses benefits and challenges. In this paper, we particularly focus on the bright side of AI and its promising potential to face our society’s grand challenges. Given this potential, different studies have already conducted valuable work by conceptualizing specific facets of AI and sustainability, including reviews on AI and Information Systems (IS) research or AI and business values. Nonetheless, there is still little holistic knowledge at the intersection of IS, AI, and sustainability. This is problematic because the IS discipline, with its socio-technical nature, has the ability to integrate perspectives beyond the currently dominant technological one as well as can advance both theory and the development of purposeful artifacts. To bridge this gap, we disclose how IS research currently makes use of AI to boost sustainable development. Based on a systematically collected corpus of 95 articles, we examine sustainability goals, data inputs, technologies and algorithms, and evaluation approaches that coin the current state of the art within the IS discipline. This comprehensive overview enables us to make more informed investments (e.g., policy and practice) as well as to discuss blind spots and possible directions for future research

Neural Natural Language Generation: A Survey on Multilinguality, Multimodality, Controllability and Learning

Developing artificial learning systems that can understand and generate natural language has been one of the long-standing goals of artificial intelligence. Recent decades have witnessed an impressive progress on both of these problems, giving rise to a new family of approaches. Especially, the advances in deep learning over the past couple of years have led to neural approaches to natural language generation (NLG). These methods combine generative language learning techniques with neural-networks based frameworks. With a wide range of applications in natural language processing, neural NLG (NNLG) is a new and fast growing field of research. In this state-of-the-art report, we investigate the recent developments and applications of NNLG in its full extent from a multidimensional view, covering critical perspectives such as multimodality, multilinguality, controllability and learning strategies. We summarize the fundamental building blocks of NNLG approaches from these aspects and provide detailed reviews of commonly used preprocessing steps and basic neural architectures. This report also focuses on the seminal applications of these NNLG models such as machine translation, description generation, automatic speech recognition, abstractive summarization, text simplification, question answering and generation, and dialogue generation. Finally, we conclude with a thorough discussion of the described frameworks by pointing out some open research directions.This work has been partially supported by the European Commission ICT COST Action “Multi-task, Multilingual, Multi-modal Language Generation” (CA18231). AE was supported by BAGEP 2021 Award of the Science Academy. EE was supported in part by TUBA GEBIP 2018 Award. BP is in in part funded by Independent Research Fund Denmark (DFF) grant 9063-00077B. IC has received funding from the European Union’s Horizon 2020 research and innovation programme under the Marie Sklodowska-Curie grant agreement No 838188. EL is partly funded by Generalitat Valenciana and the Spanish Government throught projects PROMETEU/2018/089 and RTI2018-094649-B-I00, respectively. SMI is partly funded by UNIRI project uniri-drustv-18-20. GB is partly supported by the Ministry of Innovation and the National Research, Development and Innovation Office within the framework of the Hungarian Artificial Intelligence National Laboratory Programme. COT is partially funded by the Romanian Ministry of European Investments and Projects through the Competitiveness Operational Program (POC) project “HOLOTRAIN” (grant no. 29/221 ap2/07.04.2020, SMIS code: 129077) and by the German Academic Exchange Service (DAAD) through the project “AWAKEN: content-Aware and netWork-Aware faKE News mitigation” (grant no. 91809005). ESA is partially funded by the German Academic Exchange Service (DAAD) through the project “Deep-Learning Anomaly Detection for Human and Automated Users Behavior” (grant no. 91809358)

Análise de sentimento em comentários na web

Nowadays, millions of people use the internet. They prefer to consult the Wikipedia instead of searching for information in an offline encyclopedia, prefer a travel website or a blog rather than a travel agency or a pamphlet. This type of site has helped improve people’s lives. The online reviews, for example, have the potential to provide an insight to the future buyers about the product, such as its quality. In addition, these reviews also help the marketers getting an idea about their own product. This trend has influence in many industries, like tourism or culinary. However, the true value of social media data is infrequently discovered due to the overload of information. Furthermore, it is not easily accessible sometimes. So, extracting and processing these data to obtain useful information is important. The purpose of the work described in this thesis was to develop a system which collects and analyzes Portuguese reviews about Tripadvisor hotels plus restaurants and create a web application for visualization of the information obtained. In terms of analysis, the objectives were divided in two: Multi-class and binary classification of the comments using machine learning classifiers and associate the parameters of the places (e.g. ’bedroom’) to feeling markers (e.g. ’great’) giving them a punctuation (one to five). After obtaining and pre-processing the data the sentiment analysis followed. For the first task four classifiers were used: Naive Bayes, Random Forest, Support Vector Machines (SVM) and an LSTM neural network, being LSTM the one that obtained better results (78% and 89% of accuracy for multi-class and binary classification, respectively). The second one was completed with the help of the SyntaxNet tool and neuronal network models, with various associations found and classified.Hoje em dia milhões de pessoas usam a internet. Preferem consultar a Wikipedia em vez de procurar por informações numa enciclopédia offline, preferem um site de viagens ou um blog em vez de uma agência de viagens ou de panfletos. Este tipo de sites ajudou a melhorar a vida das pessoas. Os comentários online, por exemplo, têm o potencial de fornecer uma visão aos futuros compradores sobre os produtos em avaliação, como é o caso da sua qualidade. Além disso, estes comentários ajudam também os comerciantes a ter uma ideia do seu próprio produto. Esta tendência tem assim influência em muitas indústrias, como turismo ou culinária. No entanto, o verdadeiro valor dos dados da mídia social é raramente descoberto devido à sobrecarga de informação apresentada. Sendo que por vezes esta informação também não é facilmente acessível. Assim, foi considerado importante extrair este tipo de dados (comentários) e trabalhá-los de maneira a obter informações úteis. O objetivo desta tese prende-se então em desenvolver um sistema que obtenha e analise revisões sobre hotéis e restaurantes no TripAdvisor, em português, criando por fim uma aplicação web para visualização das informações obtidas. Em termos de análise, os objetivos foram divididos em dois: classificação multiclasse e binária dos comentários usando classificadores de aprendizagem automática e associar os parâmetros dos locais (por exemplo, ’quarto’) a marcadores de opinião (por exemplo, ’ótimo’), dando-lhes uma pontuação (de um a cinco). Depois da obtenção e pré-processamento dos dados foi então feita a análise de sentimento destes. Para a primeira tarefa de análise foram utilizados quatro classificadores: Naive Bayes, Random Forest, Support Vector Machines (SVM) e uma rede neural LSTM, sendo a LSTM a que obteve melhores resultados (78% e 89% de exatidão para a classificação multiclasse e binária, respetivamente). A segunda tarefa foi completada com a ajuda da ferramenta SyntaxNet e de modelos de redes neurais, tendo sido encontradas e classificadas muitas associações.Mestrado em Engenharia de Computadores e Telemátic

Semi-automatic approaches for exploiting shifter patterns in domain-specific sentiment analysis

This paper describes two different approaches to sentiment analysis. The first is a form of symbolic approach that exploits a sentiment lexicon together with a set of shifter patterns and rules. The sentiment lexicon includes single words (unigrams) and is developed automatically by exploiting labeled examples. The shifter patterns include intensification, attenuation/downtoning and inversion/reversal and are developed manually. The second approach exploits a deep neural network, which uses a pre-trained language model. Both approaches were applied to texts on economics and finance domains from newspapers in European Portuguese. We show that the symbolic approach achieves virtually the same performance as the deep neural network. In addition, the symbolic approach provides understandable explanations, and the acquired knowledge can be communicated to others. We release the shifter patterns to motivate future research in this direction