Abstract: In order to survive on the market, companies must
constantly develop improved and new products. These products are
designed to serve the needs of their customers in the best possible
way. The creation of new products is also called innovation and is
primarily driven by a company’s internal research and development
department. However, a new approach has been taking place for some
years now, involving external knowledge in the innovation process.
This approach is called open innovation and identifies customer
knowledge as the most important source in the innovation process. This paper presents a concept of using social media posts as an external source to support the open innovation approach in its
initial phase, the Ideation phase. For this purpose, the social media
posts are semantically structured with the help of an ontology and
the authors are evaluated using graph-theoretical metrics such as
density. For the structuring and evaluation of relevant social media
posts, we also use the findings of Natural Language Processing, e.
g. Named Entity Recognition, specific dictionaries, Triple Tagger
and Part-of-Speech-Tagger. The selection and evaluation of the tools
used are discussed in this paper. Using our ontology and metrics
to structure social media posts enables users to semantically search
these posts for new product ideas and thus gain an improved insight
into the external sources such as customer needs.