Estudo sobre Métricas para Definir Reputação do Autor de Comentários em Sites de Vendas de Produtos

Conhecer a reputação do autor de textos opinativos na Web é de suma importância para o desenvolvimento de sistemas baseados em dados abertos. Este artigo apresenta um estudo sobre medidas usadas no processo de avaliação da reputação do autor em sites de vendas de produtos. Realizou-se dois experimentos com as redes neurais Multilayer Perceptron (MLP) e Radial Basis Function (RBF), sendo que a rede MLP obteve melhor desempenho. Em um terceiro experimento, comparou-se a abordagem TOP(X) original, usada para inferir os melhores comentários, com um novo modelo que utiliza rede MLP na dimensão da reputação do autor. Considerando os comentários excelentes e bons, a nova abordagem apresentou resultados significativamente superiores. Adicionalmente, foi realizado um quarto experimento com outros algoritmos de aprendizagem de máquina (AM) para observar o comportamento dos dados

Approach to define Author Reputation in Web Product Reviews using Artificial Neural Networks

Author reputation is a very important variable for evaluating web comments. However, there is no formal definition for calculating its value. This paper presents an adaptation of the approach presented by Sousa (2015) for evaluating the importance of comments about products and services available online, emphasizing measures of author reputation. The implemented adaptation consists in defining six measures for authors, used as input in a Multilayer Perceptron Artificial Neural Network. On a preliminary evaluation, the Neural Network presented an accuracy of 91.01% on the author classification process. Additionally, an experiment was conduced aiming to compare both approaches, and the results show that the adapted approach had better performance in classifying the importance of comments.Sociedad Argentina de Informática e Investigación Operativa (SADIO

Adapting the Standard SIR Disease Model in Order to Track and Predict the Spreading of the EBOLA Virus Using Twitter Data

A method has been developed to track infectious diseases by using data mining of active Twitter accounts and its efficacy was demonstrated during the West African Ebola outbreak of 2014. Using a meme based n-gram semantic usage model to search the Twitter database for indications of illness, flight and death from the spread of Ebola in Africa, principally from Guinea, Sierra Leone and Liberia. Memes of interest relate disease to location and severity and are coupled to the density of Tweets and re-Tweets. The meme spreads through the community of social users in a fashion similar to nonlinear wave propagation- like a shock wave, visualized as a spike in Tweet activity. The spreading was modeled as a system isomorphic to a modified SIR (Susceptible, Infected, Removed disease model) system of three coupled nonlinear differential equations using Twitter variables. The nonlinear terms in this model lead to feedback mechanisms that result in unusual behavior that does not always reduce the spread of the disease. The resulting geographic Tweet densities are coupled to geographic maps of the region. These maps have specific threat levels that are ported to a mobile application (app) and can be used by travelers to assess the relative safety of the region they will be in