This paper presents a Decision Support System (DSS) that helps companies with corporate reputation (CR) estimates of their respective brands by collecting provided feedbacks on their products and services and deriving state-of-the-art key performance indicators. A Sentiment Analysis Engine (SAE) is at the core of the proposed DSS that enables to monitor, estimate, and classify clients’ sentiments in terms of polarity, as expressed in public comments on social media (SM) company channels. The SAE is built on machine learning (ML) text classification models that are cross-source trained and validated with real data streams from a platform like Trustpilot that specializes in user reviews and tested on unseen comments gathered from a collection of public company pages and channels on a social networking platform like Facebook. Such crosssource opinion analysis remains a challenge and is highly relevant in the disciplines of research and engineering in which a sentiment classifier for an unlabeled destination domain is assisted by a tagged source task (Singh and Jaiswal, 2022). The best performance in terms of F1 score was obtained with a multinomial naive Bayes model: 0,87 for validation and 0,74 for testing.
In januari 2016 maakte Vitalik Buterin op het blog van Ethereum duidelijk dat blockchain en privacy moeizaam samengaan. Compliance aan de AVG is van belang, vooral vanwege de hoge boetes die door de Autoriteit Persoonsgegevens opgelegd kunnen worden. In hoeverre kan de blockchain overweg met de AVG?
MULTIFILE
Continuous monitoring, continuous auditing and continuous assurance are three methods that utilize a high degree of business intelligence and analytics. The increased interest in the three methods has led to multiple studies that analyze each method or a combination of methods from a micro-level. However, limited studies have focused on the perceived usage scenarios of the three methods from a macro level through the eyes of the end-user. In this study, we bridge the gap by identifying the different usage scenarios for each of the methods according to the end-users, the accountants. Data has been collected through a survey, which is analyzed by applying a nominal analysis and a process mining algorithm. Results show that respondents indicated 13 unique usage scenarios, while not one of the three methods is included in all of the 13 scenarios, which illustrates the diversity of opinions in accountancy practice in the Netherlands.
