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Understanding the topic of a piece of writing is typically an easy task for people. However, there are times where we need to train our computers to find topics in a collection of documents. There might be too many documents for you, a single human, to read through, or you may be interested in discovering underlying themes in a large set of texts. Enter LDA, a popular model for Topic Modeling.
After training a Phrases model with Community texts, I wanted to be able to incorporate the model into Alteryx workflows that I was using to process text, and hopefully even be able to share the model with other Alteryx users. After thinking through this, I realized it was a perfect application for the Python SDK.
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.
Ever wondered how to build a new analytic tool from scratch using the Alteryx Python SDK, but didn’t know where to start? This blog post takes you through the absolute basics to get you up and running - You’ll be creating brand new tools, connectors and advanced analytics in no time with this step-by-step beginners guide!