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Qlik Full-stack? APIs? Machine Learning?
If these topics sounds interesting to you, this series(Part-1 & 2) might be your starting point.
Today, we are going to talk about one particular area within Visual Analytics i.e., Visual Text Analytics. This tutorial will focus on the nitty-gritty of this area of research and in my next post, I will do a step-by-step tutorial of how you can actually develop the application.
With the surge in the generation of digital text on the web in the form of product reviews, descriptions, feedback, etc., there has been a demand for leveraging text mining techniques to understand and analyze these unstructured data. Typically organizations would like to be able to identify patterns, specific keywords(that make an impact), similarities, etc. through text mining. However, the challenge in analyzing hidden patterns from a large noisy text corpora can be huge and at times daunting for analysts. To mitigate the challenge in the discussion, this research area aims to bring text mining, text visualization, and Human-Computer interaction together to make sense of the data.
In the past, I have built a couple of Visual Text Analytics applications using technology stack such as - D3.js, Plotly/Dash, Python Flask(for APIs), etc., and thought it might be interesting to try developing an app using Qlik Sense’s open-sourced solutions. Primarily, for this blog, we will be looking at two of Qlik’s frameworks - Nebula.js and Picasso.js. If you are not aware of them, here is a quick gist:
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