VR Gameplay — Sentiment Analysis: Part 3
1.1 Deploy APIs on Heroku
Heroku enables developers to build, run, and operate applications entirely in the cloud. We can map the code from git repository to Heroku deployment and deploy it as public services, Then we use the other applications to call the services via the Internet.
1. Register and log in the Heroku website: https://www.heroku.com/.
2. Create new application.
3. Enter the App name and region, then click Create app button
4. In the Deploy tab, we do not need to create a pipeline.
5. Change the Deployment method to GitHub.
6. Search the repository of our APIs, which is py_sentiment_analysis.
7. Click the Connect button.
8. We do not need to enable the automatic deployment.
9. Select the branch as master and click the Deploy Branch button.
10. The successful deployment should display the following screenshot.
1.2 Test the APIs on Heroku Server
1. Install Postman. We can choose the free plan for the installation.
a. https://www.postman.com/pricing/
2. Open Postman from the start menu.
3. Create a new request.
4. Fill the request’s details and click Send button:
Method = POSTEndpoint = https://ml-sentiment-api.herokuapp.com/api/v2/resources/sentiment/vaderBody = rawRequest type = JSONRequest body = {“sentence”: “God gives us dreams at night so that we can turn them into reality during the day. So have faith and move ahead.”,“emotion”: “”}
5. The APIs on Heroku should respond the result successfully.
1.3 Available API Endpoints
The following endpoints are published on Heroku server and they can be called by using the same request parameters. The different is the NLTK analysis methods regarding the trained model, which all of them are listed in http://www.nltk.org/nltk_data/. For this project, we used Vader lexicon with neutral tone.
Conclusion
In this story, we deploy and test the APIs on Heroku to predict the tone of sentences by using NLTK Vader model.
Next
We will set up and build Android Archive (AAR) plug-in. This is the plug-in for use in Unity 3D.