Technologies and tools for semantic text analysis have matured past the point of lab experiments and prototypes. Thus, more and more companies consider to regularly use them it in various fields of business applications.
Texts - may they be economic news, customer reports, corporate websites, patents, analyst reports, social media or documents of political nature - are digitally available nowadays and can be analyszed in real time. A text database, called text corpus, especially when combined with internal and structured data, enables organizations to investigate new questions and to respond much faster to a changing environment and thus achieve strategic advantages.
Typical application fields of semantic analysis are
- competitive intelligence,
- technology monitoring, and
- market analysis, such as start-up monitoring.
- Also, e.g. sentiment analysis of social media and
- risk analysis - as common in the insurance industry - are established application areas.
In order to identify the business potential of the enterprise application of text intelligence – including the assessment of the technical as well as organizational efforts required - a solid technical understanding and good knowledge of the individual business processes are required. It is important to understand that text analytics applications do not provide automatic answers and decisions; human interpretations are is needed and part of the business solutions.
Until now, topics such as text mining and semantic text analysis have mainly been taught in computer science courses with a strong focus on technology foundations.
Nowadays, the market maturity of text analysis technology also requires master students in management and strategy to have a basic understanding of the technologies, concepts and about the diversity of enterprise applications.
|Number of credit hours per week||2|
|Presence of students||On-campus|