Sentiment analysis with AWS ML
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Webinar "Análisis de Sentimiento Utilizando Machine Learning con AWS" Veremos como analizar sentimientos a traves de lenguaje natural utilizando: Amazon Comprehend, Amazon Kinesis, Amazon Athena, y Amazon QuickSight
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Sentiment analysis with AWS ML
- 1. Analisis de Sentimiento Con ML Mauricio Ferreyra |
- 2. Qué vamos a ver hoy? Crear un sistema que collecte Tweets basados en hastag o palabras claves, entender el contenido, analizarlos y visualizarlos en un dashboard con QuickSight
- 7. Collectando Tweets Amazon EC2 AmazonKinesis Firehose bucket with objects Amazon S3
- 8. Prcesando Tweets NLP – Natural Language Processing
- 10. bucket with objects Amazon S3 Amazon Athena AWS Glue SQL Consultando el DATALAKE
- 11. Dashboard – AMAZON Quicksight Amazon QuickSight
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- Leverage Amazon Kinesis Data Firehose to easily capture, prepare, and load real-time data streams into data stores, data warehouses, and data lakes. In this example, we’ll use Amazon S3. Trigger AWS Lambda to analyze the tweets using Amazon Translate and Amazon Comprehend, two fully managed services from AWS. With only a few lines of code, these services will allow us to translate between languages and perform natural language processing (NLP) on the tweets. Leverage separate Kinesis data delivery streams within Amazon Kinesis Data Firehose to write the analyzed data back to the data lake. Leverage Amazon Athena to query the data stored in Amazon S3. Build a set of dashboards using Amazon QuickSight.