![]() ![]() Third, we shall repeat the first two integrating the offline topics with the equivalent online topics (i.e. The abstracts will then be tokenised to produce individual words. Second, the embeddings will be trained with scientific abstracts from each topic. First, the compilation of consolidated bodies of offline marketing science research such as retail or advertising. We shall then conduct a three-step process adopted from Tshitoyan et al. This will result in a vocabulary formed by the extracted contents. ![]() After removing irrelevant abstracts, the remaining relevant ones will be tokenised. Word embeddings will be trained with marketing science abstracts retrieved from scientific databases (e.g. Our suggested method for marketing science will be based on the developments made in chemicals and materials. In these fields, knowledge extraction and relationships are made using massive bodies of scientific literature using CrossRef Application Programming Interface (API) to retrieve large lists of article Digital Object Identifiers (DOIs). In this regard, the most advanced developments have been made in the disciplines of chemistry and materials (e.g. The combination of NLP and text mining techniques enables scientific literature text mining with NLP. In AI, NLP is used to analyse large amounts of natural language data. Text mining is the use of algorithms for extracting information from text documents such as scientific articles. AI techniques such as text mining and natural language processing (NLP) can assist in removing the bottleneck. The rapid increase in the rate of research points out the need for techniques that can simplify its use. Recognising new questions or hypotheses will be increasingly challenging. Keeping up is untenable for researchers even within specialised fields. Research overload is a bottleneck for scientific development. Thousands of papers are published each year. Scientific progress is disseminated in scientific publications, which are growing exponentially. This could automate literature review and formulation of hypotheses. Marketing science would benefit from adopting AI-based technologies for text mining, analysis and predictive writing. The media company runs the conservative Independent Journal Review.We discuss the development of a machine-based research literature reading methodology for marketing science based on artificial intelligence (AI) developments made in other fields. Virginia-based Disruptor Capital, an investment firm founded by Pete Snyder, a former Republican candidate for lieutenant governor in Virginia, is investing $1.5 million into Media Group of America, according to the Washington Post. The company analyses videos for their marketing potential. Veenomeborrowed close to $400,000, according to an SEC filing. In September, it won a $3.82 million contract with the Defense Information Systems Agency, according to Businessweek. One of the investors appears to be Peter Gerhard, the founder of G Capital Fund. Tangible Security, a cybersecurity company based in McLean, Va., raised $6 million, according to an SEC filing. Multilingual data analytics software company Babel Street, which is based in Reston, Va., has raised at least $925,000 out of a prospective $2.25 million round, according to an SEC filing. Leading the round, are GrowthWorks Atlantic Venture Fund, Silicon Valley inventor Ronjon Nag, and D.C.-based early stage investment fund Fortify Ventures. Introhive, a company that helps businesses mine their contact data, has raised $6.7 million, according to an SEC filing.
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