Earlier this 12 months, one in every of our customers made a put up about how she used Domo and Jupyter Notebooks to run emotion detection, a kind of sentiment evaluation, on app critiques from the Android App Retailer.

We determined that we wished to share with everyone some code on how this works. For this evaluation, we acquire critiques and attributes from a few of our favourite meals supply apps, mix the data right into a dataset, and create a dashboard exhibiting the sentiment evaluation of every overview.

Kendall Ruber shared her code with us, and now we have recreated a dashboard beneath in Domo utilizing our Jupyter Pocket book integration. Our Jupyter integration permits for extra superior evaluation strategies and fashions to be developed and deployed solely inside Domo with ease.

As an illustration, as a substitute of doing one thing out of the field similar to a phrase cloud to try to determine what sentiment is (which may be completely different from the App Retailer Rankings), we will get a greater concept of our customers’ sentiment by using Python’s Pure Language Processing strategies inside Domo. We run the critiques by an emotion detection mannequin, one Kendall discovered on Hugging Face, and with minimal effort now we have an ML mannequin developed and deployed.

We can also reap the benefits of the scheduling choices inside Domo. As an illustration, this Dashboard beneath will replace every day at 09:00 UTC. Different choices are additionally out there, and present Domo customers will discover the choices acquainted.

Moreover, we usher in information from the Google Play Retailer API by python code written in Jupyter. This places information from third occasion APIs that don’t but have a Domo Connector inside attain of your Domo surroundings.

Lastly, we additionally now help sharing and collaborating on notebooks, in addition to integration with Accounts inside Domo.

Dashboard beneath, code and directions discovered on our GitHub web site.