You need to have the following tech installed locally:

• dotnet core 3.1 (LTS)
• node 12.16 (LTS)
• docker version 18.x.
• docker-compose version 1.23.x.

• clone this repo
• run npm install in /scripts
• run node scripts/setup
• run docker-compose up

run node script/run-all-unit-tests.js

First run the system and let tracker execute a full article fetch. Make sure that tracker has the writeArticlesToFile env var set to true. Then navigate to the script folder. In the script folder run

node add-to-dataset-and-re-train-model.js

(more about the dataset under Challanges below)

The idea is to track how Swedish media reports on different subjects by using sentiment analysis. This hobby project scratches two itches at once for me;

1. I find the idea interesting
2. I use this solution to learn, try and evaluate different things ... (hence the overengineering, complex architecture and different versions of a single component)

         tracker (tracks and extracts articles containing keywords)  
            |
            |
         analyzer (analyzes sentiment)
            |
            |
         storage (stores analysis)
        /       \          
       /         \
  visualizer     bot

• Kubernetes (yaml files)
• Docker & Docker Compose
• dotnet core 3 (web/service and worker)
• ML.NET
• Sentiment (AFINN-based sentiment analysis for Node.js)
• gRPC
• NodeServices in .net core (soon to be replaced...)
• Ef core
• Postgres
• RabbitMQ
• Client side Blazor (WebAssembly) - hence the initial load time :)
• Vue.js (Currently porting the client, see visualizer2)
• The mediator pattern (without a library)
• CQRS
• Some hexagonal architecture principles in a microservices setting

I have not been able to find a good Swedish dataset to train my ML.NET model with. So, for now, I am doing the following:

I am using a Swedish translated AFINN lexicon and word lists I found on Kaggle to generate a dataset from actual articles pulled down by the tracker component.

See /dataset-gen/index.js and scripts/add-to-dataset-and-re-train-model.js and the code in tracker that writes articles to a file to see how this works.

Its actually working better than I thought it would. But the current model still gets it really wrong sometimes so I am using an AFINN server as a safety net (see analyzer - Engine.cs).