Using sentiment analysis to evaluate the impact of the COVID-19 outbreak on Italy’s country reputation and stock market performance

During the recent Coronavirus disease 2019 (COVID-19) outbreak, the microblogging service Twitter has been widely used to share opinions and reactions to events. Italy was one of the frst European countries to be severely afected by the outbreak and to establish lockdown and stay-at-home orders, potentially leading to country reputation damage. We resort to sentiment analysis to investigate changes in opinions about Italy reported on Twitter before and after the COVID-19 outbreak. Using diferent lexicons-based methods, we fnd a breakpoint corresponding to the date of the frst established case of COVID-19 in Italy that causes a relevant change in sentiment scores used as a proxy of the country’s reputation. Next, we demonstrate that sentiment scores about Italy are associated with the values of the FTSE-MIB index, the Italian Stock Exchange main index, as they serve as early detection signals of changes in the values of FTSE-MIB. Lastly, we evaluate whether diferent machine learning classifers were able to determine the polarity of tweets posted before and after the outbreak with a diferent level of accuracy

Global attractors for a three-dimensional conserved phase-field system with memory

We consider a conserved phase-field system on a tridimensional bounded domain. The heat conduction is characterized by memory effects depending on the past history of the (relative) temperature [\vartheta] . These effects are represented through a convolution integral whose relaxation kernel [k] is a summable and decreasing function. Therefore the system consists of a linear integrodifferential equation for [\vartheta] which is coupled with a viscous Cahn-Hilliard type equation governing the order parameter [\chi] . The latter equation contains a nonmonotone nonlinearity [\phi] and the viscosity effects are taken into account by the term [-\alpha \Delta\chi_t] , for some [\alpha \geq 0] . Thus, we formulate a Cauchy-Neumann problem depending on [\alpha ] . Assuming suitable conditions on [k] , we prove that this problem generates a dissipative strongly continuous semigroup [S^\alpha (t)] on an appropriate phase space accounting for the past histories of [\vartheta] as well as for the conservation of the spatial means of the enthalpy [\vartheta+\chi] and of the order parameter. We first show, for any [\alpha \geq 0] , the existence of the global attractor [\mathcal A_\alpha ] . Also, in the viscous case ( [\alpha > 0] ), we prove the finiteness of the fractal dimension and the smoothness of [\mathcal A_\alpha ]

Enhancing CFD predictions in shape design problems by model and parameter space reduction

In this work we present an advanced computational pipeline for the approximation and prediction of the lift coefficient of a parametrized airfoil profile. The non-intrusive reduced order method is based on dynamic mode decomposition (DMD) and it is coupled with dynamic active subspaces (DyAS) to enhance the future state prediction of the target function and reduce the parameter space dimensionality. The pipeline is based on high-fidelity simulations carried out by the application of finite volume method for turbulent flows, and automatic mesh morphing through radial basis functions interpolation technique. The proposed pipeline is able to save 1/3 of the overall computational resources thanks to the application of DMD. Moreover exploiting DyAS and performing the regression on a lower dimensional space results in the reduction of the relative error in the approximation of the time-varying lift coefficient by a factor 2 with respect to using only the DMD

Threshold-based Naïve Bayes classifier

The Threshold-based Naive Bayes (Tb-NB) classifier is introduced as a (simple) improved version of the original Naive Bayes classifier. Tb-NB extracts the sentiment from a Natural Language text corpus and allows the user not only to predict how much a sentence is positive (negative) but also to quantify a sentiment with a numeric value. It is based on the estimation of a single threshold value that concurs to define a decision rule that classifies a text into a positive (negative) opinion based on its content. One of the main advantage deriving from Tb-NB is the possibility to utilize its results as the input of post-hoc analysis aimed at observing how the quality associated to the different dimensions of a product or a service or, in a mirrored fashion, the different dimensions of customer satisfaction evolve in time or change with respect to different locations. The effectiveness of Tb-NB is evaluated analyzing data concerning the tourism industry and, specifically, hotel guests' reviews from all hotels located in the Sardinian region and available on Booking.com. Moreover, Tb-NB is compared with other popular classifiers used in sentiment analysis in terms of model accuracy, resistance to noise and computational efficiency

Campañas medioambientales contra empresas forestales: ¿Cuál es el objetivo de estas campañas?

Campaigns by environmental non-governmental organisations (ENGOs) can have far reaching consequences in determining the policies of governments and corporations. This paper examines campaigns targeting forestry companies to determine what makes a successful campaign. Over forty ENGOs completed a questionnaire defining what they perceive to constitute a successful campaign. The responses were analysed using Analytical Hierarchy Process. The results showed that campaigns by ENGOs have two main targets: changes in laws and the target group implementing the campaign’s recommendation(s). Achieving these targets, for most, constitute a successful campaign. Subsequently, representatives of seven ENGOs were questioned to attain their perspectives of the results in comparison to campaigns they are conducting against forest enterprises. They supported the results of the questionnaire, but also felt that there are various other factors that need to be considered (e.g. the campaign’s timeframe and the possibility of having hidden targets) that increase the issue’s complexity.Campañas llevadas a cabo por organizaciones no gubernamentales ambientalistas (ONGsA) pueden tener importantes consecuencias a la hora de influenciar las políticas tanto de gobiernos como de corporaciones industriales. Este artículo se centra en el estudio de campañas cuyo blanco son las empresas madereras, analizando que condiciones deben cumplir dichas campañas para poder ser consideradas como exitosas. Para ello, más de cuarenta ONGsA rellenaron un cuestionario en el que se les pedía que indicaran su opinión sobre que define una campaña exitosa. Las respuestas fueron analizadas utilizando un Proceso Analítico Jerárquico (AHP). Los resultados mostraron que las campañas de las ONGsA tienen dos objetivos principales, obtener cambios en las leyes y que la compañía o gobierno objetivo de la campaña cumpla las recomendaciones propuestas. A posteriori, los representantes de siete ONGsA, con campañas ambientales en curso contra empresas madereras, fueron preguntados sobre si los resultados obtenidos a través del análisis AHP estaban en concordancia con las perspectivas para sus campañas ambientales. Como resultado, se obtuvo que si bien sus perspectivas coincidían con los resultados del análisis, existen otros factores a tener en cuenta (por ejemplo el marco temporal de la campaña y la posibilidad de afectar objetivos no explícitamente señalados) que aumentan la complejidad del problema