Twitter Sentiment Analysis with Synapse Analytics | Real time Stream | E2E Big Data Pipeline | Spark

 

witter Sentiment Analysis with Synapse Analytics | Real time Stream | E2E Big Data Pipeline | Spark #azure synapse tutorial #azure synapse analytics tutorial #Big Data #Sentiment Analysis Twitter sentiment analysis using Synapse Analytics | Real time Sentiment Analysis | End to End Big Data Pipeline Build Big data Pipeline in 1 Hrs. Ingestion to Visualization End to End- From Subscribing tweet to visualize Azure Event Hub, Azure stream analytics, Azure Synapse Analytics Spark Pool, SQL Pool, Pipelines Cognitive services, Sentiment Analysis Power BI Azure data platform end2end his example scenario demonstrates how to use the extensive family of Azure Data Services to build a modern data platform capable of handling the most common data challenges in an organization. The solution described in this article combines a range of Azure services that will ingest, store, process, enrich, and serve data and insights from different sources (structured, semi-structured, unstructured, and streaming). In this tutorial, you'll learn how to easily enrich your data in Azure Synapse Analytics with Azure Cognitive Services. You'll use the Text Analytics capabilities to perform sentiment analysis. This article teaches you how to build a social media sentiment analysis solution by bringing real-time Twitter events into Azure Event Hubs. You write an Azure Stream Analytics query to analyze the data and store the results for later use or create a Power BI dashboard to provide insights in real-time. Social media analytics tools help organizations understand trending topics. Trending topics are subjects and attitudes that have a high volume of posts on social media. Sentiment analysis, which is also called opinion mining, uses social media analytics tools to determine attitudes toward a product or idea. Real-time Twitter trend analysis is a great example of an analytics tool because the hashtag subscription model enables you to listen to specific keywords (hashtags) and develop sentiment analysis of the feed. Scenario: Social media sentiment analysis in real time A company that has a news media website is interested in gaining an advantage over its competitors by featuring site content that is immediately relevant to its readers. The company uses social media analysis on topics that are relevant to readers by doing real-time sentiment analysis of Twitter data. To identify trending topics in real time on Twitter, the company needs real-time analytics about the tweet volume and sentiment for key topics.