Almost-final checks

Author: madeline-underwood

content/learning-paths/servers-and-cloud-computing/sentiment-analysis-eks/understand-the-basics.md

@@ -9,32 +9,46 @@ layout: learningpathall
 ## What is Sentiment Analysis?
 
 * Sentiment analysis, sometimes called *opinion mining*,  is a natural language processing (NLP) technique used to identify and categorize sentiment expressed in digital text. 
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 * Sentiment analysis uses tools to scan text and decipher the emotion behind the message, which might broadly be interpreted as positive, negative, or neutral.  
 
 ## What can Sentiment Analysis achieve, and why analyze posts on X?
 
 * Sentiment analysis can help identify trends and patterns, and inform predictions.
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 * Sentiment analysis provides insights into how people feel about a particular topic or issue, and can help to identify emerging viewpoints.
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 * It is a scalable way of providing organizations and businesses with valuable data such as insights into user feedback, which can then be used in reputation management.
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 * Tracking real-time changes enables you to understand sentiment patterns and make informed decisions promptly, allowing for timely and appropriate actions.
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 * X is one of the most popular social media platforms, and provides a wealth of rapidly-changing information about public opinion, trends, and events.
 
+{{% notice Note %}}
+From 2023, X is the new name for the social media platform formerly known as Twitter. 
+"Tweets" are also now known as "posts".
+{{% /notice %}}
 
 ## Solution Architecture
 
-Real-time sentiment analysis is a computationally-intensive task and can rapidly consume resources and increase costs if not managed effectively. 
+* Real-time sentiment analysis is a compute-intense task and can rapidly consume resources and increase costs if not managed effectively. 
 
-Using an Arm-based Amazon EKS cluster can address these issues by offering flexibility, strong performance and cost efficiencies.  
+* Using an Arm-based Amazon EKS cluster can address these issues by offering flexibility, strong performance and cost efficiencies.  
 
-Figure 1 shows the solution architecture:
+    Figure 1 shows the solution architecture that this Learning Path uses:
 
 ![sentiment analysis #center](_images/Sentiment-Analysis.png "Figure 1: Sentiment Analysis Architecture." )
 
 The technology stack for this solution includes the following steps:
 
 - Use the X developer API to fetch posts based on certain keywords.
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 - Process the captured data using Amazon Kinesis.
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 - Run a sentiment analysis model to categorize the text and classify the tone.
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 - Process the sentiment of the posts using Apache Spark streaming API.
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 - Use Elasticsearch and Kibana to store the processed Tweets and showcase the activity on a dashboard.
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 - Monitor the CPU and RAM resources of the Amazon EKS cluster with Prometheus and Grafana.