Tracking Dyson’s Public Image Using 46,166 Tweets

Tracking Dyson’s Public Image Using 46,166 Tweets

A Lesson In Google Natural Language API and BigQuery. The pipeline for getting Tweet data from the VM Instance to the BigQuery database was set up using a DataFlow template.

This project began, as all things seem to do nowadays, with COVID-19. With the amount of free time I was gifted, I decided the only logical way to spend it was programming. As an undergraduate at the Dyson Institute, I thought it would be interesting to see how the wider company was perceived by the public. So, armed with £300 worth of free Google Cloud credits I decided to give it a go.

It just so happened that the week I let my finished code run was the week of the largest redundancies in company history. Meaning in no way to make light of hundreds of people losing their jobs, it did end up being the perfect test environment for this project, and made for some very interesting results. If you don’t care about how I went about making this work, skip to the end for a breakdown of the findings.

I, in my boundless wit, decided to name this project “Dyson-timent”.

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