| Popis: |
From the perspective of marketing studies, a crisis of reputation is interpreted as such post factum, when measurable financial loss is induced. However, it is highly demanded to discover its signs as early as possible for risk management purposes. Here is where artificial intelligence finds its application since the start of the Facebook era. Emotion- or sentiment-classification algorithms, based on BiLSTM neural networks or transformer architectures, achieve very good F1 scores. Nevertheless, the scholarly literature offers very few approaches to the detection of reputational crises ante factum from an NLP point of view. At the same time, not every peak of mentions with negative sentiment equals a crisis of reputation by definition. There exist ample general sentiment classification tools dedicated to a specific social medium, e.g. Twitter, while reputational crises often expand over various Internet sources. However, they also tend to be highly unpredictable in the way they appear and spread online. Moreover, very few studies of their development have so far been conducted from the perspective of NLP tool-design. Therefore, in our work we try to answer the question: how can we track reputational crises fast and precisely in multiple communication channels, and what do current NLP methods offer with this respect? For this purpose we have: consulted Internet monitoring experts and, defined major crisis topics for three business domains, built and tested three different approaches to crisis detection (a HAN-based emotion detection model, heuristic crisis detection models for predefined risks, a statistical mention peak analysis tool with an ML-based summarization algorithm) to track most Internet sources and cover both explicit and implicit content and performed an analysis of 15 reputational crises in online sources in Poland. We offer a comparative analysis of NLP tools of qualitative semantic methods applied to a study of real-life reputational crises that appeared in Poland within the last two decades. |
