A substantial number of international corporations have been affected by corruption. The research presented in this paper introduces the Integrity Risks Monitor, an analytics dashboard that applies Web Intelligence and Deep Learning to english and germanspeaking documents for the task of (i) tracking and visualizing past corruption management gaps and their respective impacts, (ii) understanding present and past integrity issues, (iii) supporting companies in analyzing news media for identifying and mitigating integrity risks.Afterwards, we discuss the design, implementation, training and evaluation of classification components capable of identifying English documents covering the integrity topic of corruption. Domain experts created a gold standard dataset compiled from Anglo-American media coverage on corruption cases that has been used for training and evaluating the classifier. The experiments performed to evaluate the classifiers draw upon popular algorithms used for text classification such as Naïve Bayes, Support Vector Machines (SVM) and Deep Learning architectures (LSTM, BiLSTM, CNN) that draw upon different word embeddings and document representations. They also demonstrate that although classical machine learning approaches such as Naïve Bayes struggle with the diversity of the media coverage on corruption, state-of-the art Deep Learning models perform sufficiently well in the project's context. CCS CONCEPTS• Information systems → Data analytics; • Computing methodologies → Neural networks; • Applied computing → Economics; Annotation.
ZusammenfassungUnternehmensbewertungen in der Biotech-Branche, Pharmazie und Medizintechnik stellen eine anspruchsvolle Aufgabe dar, insbesondere bei Berücksichtigung der einzigartigen Risiken, denen Biotech-Startups beim Eintritt in neue Märkte ausgesetzt sind. Unternehmen, die auf globale Bewertungsdienstleistungen spezialisiert sind, kombinieren daher Bewertungsmodelle und Erfahrungen aus der Vergangenheit mit heterogenen Metriken und Indikatoren, die Einblicke in die Leistung eines Unternehmens geben. Dieser Beitrag veranschaulicht, wie automatisierte Wissensidentifikation, -extraktion und -integration genutzt werden können, um (i) zusätzliche Indikatoren zu ermitteln, die Einblicke in den Erfolg eines Unternehmens in der Produktentwicklung geben und um (ii) arbeitsintensive Datensammelprozesse zur Unternehmensbewertung zu unterstützen.
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