@misc{Nazemi2019c,

title = {Visual Text Analytics for Technology and Innovation Management},

author = {Kawa Nazemi and Dirk Burkhardt},

url = {https://www.openrheinmain.org/2019/presentations/visual_text_analytics_for_technology_and_innovation_management.pdf, Presentation as PDF},

doi = {10.5281/zenodo.3408391},

year  = {2019},

date = {2019-09-13},

abstract = {Through coupling of Data Mining, Visual Analytics and Business Analytics techniques, we created a novel solution for strategic market analysis with focus on early trend recognition. As fundament, we are able to consider a variety of text data, as for instance research publications available from a number of (open access) digital libraries, reports and other data from companies, web data about markets as well as news from companies or social media data etc. In an advanced and unified processing pipeline, the information is extracted and mined for a variety of analytical purposes. Via an interactive analysis user-interface, domain experts are able to analysis strong and weak signals in perspective of upcoming trends.},

howpublished = {Presented at OpenRheinMain Conference (ORM2019), 13 September 2019, Darmstadt, Germany},

keywords = {Business Analytics, Innovation Management, Technology Management, Text Analysis, Trend Analytics, Visual Text Analytics},

pubstate = {published},

tppubtype = {misc}

}