  Idea generation is the core activity of innovation. Digital data sources,
which are sources of innovation, such as patents, publications, social media,
websites, etc., are increasingly growing at unprecedented volume. Manual idea
generation is time-consuming and is affected by the subjectivity of the
individuals involved. Therefore, the use machine-driven data analytics
techniques to analyze data to generate ideas and support idea generation by
serving users is useful. The objective of this study is to study state-of
the-art machine-driven analytics for idea generation and data sources, hence
the result of this study will generally server as a guideline for choosing
techniques and data sources. A systematic literature review is conducted to
identify relevant scholarly literature from IEEE, Scopus, Web of Science and
Google Scholar. We selected a total of 71 articles and analyzed them
thematically. The results of this study indicate that idea generation through
machine-driven analytics applies text mining, information retrieval (IR),
artificial intelligence (AI), deep learning, machine learning, statistical
techniques, natural language processing (NLP), NLP-based morphological
analysis, network analysis, and bibliometric to support idea generation. The
results include a list of techniques and procedures in idea generation through
machine-driven idea analytics. Additionally, characterization and heuristics
used in idea generation are summarized. For the future, tools designed to
generate ideas could be explored.
