Accurate retrieval of corporate reputation from online media using machine learning

• Autorzy: Klein Achim , Riekert Martin , Dinev Velizar

Identyfikatory

DOI

10.15439/2019F169

• Konferencja: Federated Conference on Computer Science and Information Systems (14 ; 01-04.09.2019 ; Leipzig, Germany)
• Języki publikacji: EN

Abstrakty

EN

Corporate reputation is an economic asset and its accurate measurement is of increasing interest in practice and science. This measurement task is difficult because reputation depends on numerous factors and stakeholders. Traditional measurement approaches have focused on human ratings and surveys, which are costly, can be conducted only infrequently and emphasize financial aspects of a corporation. Nowadays, online media with comments related to products, services, and corporations provides an abundant source for measuring reputation more comprehensively. Against this backdrop, we propose an information retrieval approach to automatically collect reputation-related text content from online media and analyze this content by machine learning-based sentiment analysis. We contribute an ontology for identifying corporations and a unique dataset of online media texts labelled by corporations' reputation. Our approach achieves an overall accuracy of 84.4%. Our results help corporations to quickly identify their reputation from online media at low cost.

Słowa kluczowe

EN

business data processing; data mining; information retrieval; artificial intelligence; natural language processing; text analysis

PL

przetwarzanie danych biznesowych; eksploracja danych; wyszukiwanie informacji; sztuczna inteligencja; przetwarzanie języka naturalnego; analiza tekstu

• Wydawca: Polskie Towarzystwo Informatyczne
• Czasopismo: Annals of Computer Science and Information Systems
• Rocznik: 2019
• Tom: Vol. 18
• Strony: 43--46
• Opis fizyczny: Bibliogr. 40 poz., tab.

Twórcy

autor

Klein Achim

• Information Systems 2, University of Hohenheim, 70599 Stuttgart, Germany

autor

Riekert Martin

• Information Systems 2, University of Hohenheim, 70599 Stuttgart, Germany

autor

Dinev Velizar

• Information Systems 2, University of Hohenheim, 70599 Stuttgart, Germany

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Uwagi

1. Track 1: Artificial Intelligence and Applications

2. Technical Session: 14th International Symposium Advances in Artificial Intelligence and Applications

3. Opracowanie rekordu ze środków MNiSW, umowa Nr 461252 w ramach programu "Społeczna odpowiedzialność nauki" - moduł: Popularyzacja nauki i promocja sportu (2020).