Artificial Intelligence, Social Media and Supply Chain Management: The Way Forward

Supply chain management (SCM) is a complex network of multiple entities ranging from business partners to end consumers. These stakeholders frequently use social media platforms, such as Twitter and Facebook, to voice their opinions and concerns. AI-based applications, such as sentiment analysis, allow us to extract relevant information from these deliberations. We argue that the context-specific application of AI, compared to generic approaches, is more efficient in retrieving meaningful insights from social media data for SCM. We present a conceptual overview of prevalent techniques and available resources for information extraction. Subsequently, we have identified specific areas of SCM where context-aware sentiment analysis can enhance the overall efficiency

Analyzing Document Collections via Context-Aware Term Extraction

In large collections of documents that are divided into predefined classes, the differences and similarities of those classes are of special interest. This paper presents an approach that is able to automatically extract terms from such document collections which describe what topics discriminate a single class from the others (discriminating terms) and which topics discriminate a subset of the classes against the remaining ones (overlap terms). The importance for real world applications and the effectiveness of our approach are demonstrated by two out of practice examples. In a first application our predefined classes correspond to different scientific conferences. By extracting terms from collections of papers published on these conferences, we determine automatically the topical differences and similarities of the conferences. In our second application task we extract terms out of a collection of product reviews which show what features reviewers commented on. We get these terms by discriminating the product review class against a suitable counter-balance class. Finally, our method is evaluated comparing it to alternative approaches