Automated Sentiment Analysis of Opinions on ChatGPT

<p>Today, data analytics enabled by the cloud is increasingly popular in many industries. Companies are being introduced to cloud technologies and face challenges identifying business use cases that justify building modern cloud-enabled solutions. In this article, we&rsquo;ll walk you through how we built a complete data analysis solution to understand the public&rsquo;s Twitter perspective on the very popular generative AI technology &mdash; ChatGPT. To build our scalable, automated solution we leveraged Google Cloud services including Cloud Storage, Dataflow, Google BigQuery, Google Kubernetes Engine (GKE), and Cloud Build, as well as Grafana and Bitbucket.</p> <h1>Solution overview</h1> <p>The first step is to store our ChatGPT Twitter data in Cloud Storage. This is then picked up by Dataflow. Dataflow processes the data and stores it in BigQuery. A Kubernetes cluster hosts Grafana on multiple nodes, in multiple pods, and a load balancer distributes traffic. A CI/CD pipeline leverages Bitbucket, where all the Terraform code is stored &mdash; which is then built and deployed using CI/CD service Cloud Build.</p> <p><a href="https://medium.com/slalom-technology/automated-sentiment-analysis-of-opinions-on-chatgpt-bd524f22ef55"><strong>Click Here</strong></a></p>