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Word embeddings are vector representations of words, where more similar words will have similar locations in vector space. First developed by a team of researchers at Google led by Thomas Mikolov, and discussed in the paper Efficient Estimation of Word Representations in Vector Space, word2vec is a popular group of models that produce word embeddings by training shallow neural networks. In this blog post, we apply a word2vec model to the Alteryx Community texts to develop Alteryx-specific word embeddings.
Reproducibility, the open sharing of data, and expanding on the research of others are all at the heart of the scientific process, and we live in an exciting time where it is more possible than ever. This year's Inspire Europe Closing Keynote speaker Dr. Ben Goldacre has recently published a paper examining compliance with the European Commission's guideline that all Clinical Trials registered in the EU Clinical Trials Register must report results to the European Medicines Agency within 12 months of the trial's completion. The bulk of the paper's analysis was performed in the statistical software Stata. With tools like Alteryx or Python, we have easy and open-source ways to process data and derive new knowledge. In this blog, we reproduce some of Goldacre et al.'s analysis in Alteryx and Python and provide both formats for you to further explore the data on your own.