Before this year, one of our end users created a post about how she made use of Domo and Jupyter Notebooks to run emotion detection, a variety of sentiment analysis, on app evaluations from the Android Application Shop. We made the decision that we wanted to share with most people some code on how this performs. For this examination, we acquire reviews and characteristics from some of our most loved foodstuff shipping applications, blend the data into a dataset, and generate a dashboard demonstrating the sentiment analysis of each critique. Kendall Ruber shared her code with us, and we have recreated a dashboard beneath in Domo working with our Jupyter Notebook integration. Our Jupyter integration permits for additional innovative analysis procedures and styles to be developed and deployed solely inside of Domo with ease. For instance, in its place of performing a thing out of the box these as a word cloud to attempt and figure out what sentiment is (which can be various from the Application Store Rankings), we can get a improved idea of our users’ sentiment by utilizing Python’s Normal Language Processing approaches inside of Domo. We run the assessments by way of an emotion detection model, 1 Kendall discovered on Hugging Facial area, and with minimal energy we have an ML model made and deployed. We also can take benefit of the scheduling possibilities in just Domo. For instance, this Dashboard down below will update day by day at 09:00 UTC. Other possibilities are also obtainable, and present-day Domo consumers will uncover the selections acquainted. Additionally, we deliver in information from the Google Perform Shop API by way of python code published in Jupyter. This places info from 3rd bash APIs that really don’t nevertheless have a Domo Connector in just get to of your Domo surroundings. Ultimately, we also now help sharing and collaborating on notebooks, as very well as integration with Accounts inside Domo. Dashboard down below, code and directions observed on our GitHub web-site.
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