Mapping innovation in companies for the purpose of official statistics is usually done through business surveys. However, this traditional approach faces several drawbacks like a lack of responses, response bias, low frequency, and high costs. Therefore, possible solutions like text-based models have been developed to complement or substitute traditional business surveys. Web scraped company websites are used as input texts for these models. Previous research often focuses on the classification of companies into innovative or non-innovative through these models. This paper makes use of web scraping and text-based models in order to map the business innovation in Flanders. What differentiates this research from previously published work, is the special focus on the different types of innovation, discovered through topic modeling. More specifically, the scraped web texts are used to identify innovative economic sectors or topics, and to classify firms into these topics. The Flemish firms considered in this research are those that participated in the CIS 2019. It was found that the Top2Vec model can discover topics related to innovation within a large unstructured text corpus. Consequently, the Lbl2Vec model can use the Top2Vec output as an input to classify firms into the discovered topics. Therefore, this paper shows the potential of combining Top2Vec and Lbl2Vec model for discovering topics (or sectors) and classifying companies into these topics which results in an additional parameter for mapping innovation in different regions.