In this blog post we’ll show you how you can use Amazon Translate, Amazon Comprehend, Amazon Kinesis, Amazon Athena, and Amazon QuickSight to build a natural-language-processing (NLP)-powered social media dashboard for tweets. Social media interactions between organizations and customers deepen brand awareness.
These conversations are a low-cost way to acquire leads, improve website traffic, develop customer relationships, and improve customer service. In this blog post, we’ll build a serverless data processing and machine learning (ML) pipeline that provides a multi-lingual social media dashboard of tweets within Amazon QuickSight.
We’ll leverage API-driven ML services that allow developers to easily add intelligence to any application, such as computer vision, speech, language analysis, and chatbot functionality simply by calling a highly available, scalable, and secure endpoint. These building blocks will be put together with very little code, by leveraging serverless offerings within AWS.
For this blog post, we will be performing language translation and natural language processing on the tweets flowing through the system. In addition to building a social media dashboard, we want to capture both the raw and enriched datasets and durably store them in a data lake.
This allows data analysts to quickly and easily perform new types of analytics and machine learning on this data. Throughout this blog post, we’ll show how you can do the following: The following diagram shows both the ingest (blue) and query (orange) flows. Read more from aws.amazon.com…
thumbnail courtesy of amazon.com