# Step 5: Visualizing Amazon Comprehend output in Quick
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After storing the Amazon Comprehend results in tables, you can connect to and visualize the data with Quick. Quick is an AWS managed business intelligence (BI) tool for visualizing data. Quick makes it easy to connect to your data source and create powerful visuals. In this step, you connect Quick to your data, create visualizations that extract insights from the data, and publish a dashboard of visualizations.

**Topics**
+ [Prerequisites](#tutorial-reviews-visualize-prereqs)
+ [Give Quick access](#tutorial-reviews-visualize-access)
+ [Import the datasets](#tutorial-reviews-visualize-import)
+ [Create a sentiment visualization](#tutorial-reviews-visualize-sentiment)
+ [Create an entities visualization](#tutorial-reviews-visualize-entities)
+ [Publish a dashboard](#tutorial-reviews-visualize-dashboard)
+ [Clean up](#tutorial-reviews-visualize-clean)

## Prerequisites
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Before you begin, complete [Step 4: Preparing the Amazon Comprehend output for data visualization](tutorial-reviews-tables.md).

## Give Quick access
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To import the data, Quick requires access to your Amazon Simple Storage Service (Amazon S3) bucket and Amazon Athena tables. To give Quick access to your data, you must be signed in as a QuickSight administrator and have access to edit the resource permissions. If you are unable to complete the following steps, review the IAM prerequisites from the overview page [Tutorial: Analyzing insights from customer reviews with Amazon Comprehend](tutorial-reviews.md).

**To give Quick access to your data**

1. Open the [Quick console](https://quicksight.aws.amazon.com/sn/start).

1. If this is the first time you have used Quick, the console prompts you to create a new administrator user by providing an email address. For **Email address**, enter the same email address as your AWS account. Choose **Continue**.

1. After signing in, choose your profile name in the navigation bar and choose **Manage QuickSight**. You must be signed in as an administrator to view the **Manage QuickSight** option.

1. Choose **Security and permissions**.

1. For **QuickSight access to AWS services**, choose **Add or remove**.

1. Choose **Amazon S3**.

1. From **Select Amazon S3 buckets**, choose your S3 bucket for both **S3 Bucket** and **Write permissions for Athena Workgroup**.

1. Choose **Finish**.

1. Choose **Update**.

## Import the datasets
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Before creating visualizations, you must add the sentiment and entities datasets to Quick. You do this with the Quick console. You import your unnested sentiment and unnested entities tables from Amazon Athena.

**To import your datasets**

1. Open the [Quick console](https://quicksight.aws.amazon.com/sn/start).

1. In the navigation bar, in **Datasets**, choose **New dataset**.

1. For **Create a Data Set**, choose **Athena**.

1. For **Data source name**, enter `reviews-sentiment-analysis` and choose **Create data source**.

1. For **Database**, choose the database `comprehend-results`.

1. For **Tables**, choose the sentiment table `sentiment_results_final` and then choose **Select**.

1. Choose **Import to SPICE for quicker analytics** and choose **Visualize**. SPICE is QuickSight's in-memory calculation engine that provides faster analyses than direct querying when creating visualizations.

1. Return to the Quick console and choose **Datasets**. Repeat steps 1-7 to create an entities dataset, but make the following changes:

   1. For **Data source name**, enter `reviews-entities-analysis`.

   1. For **Tables**, choose the entities table `entities_results_final`.

## Create a sentiment visualization
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Now that you can access your data in Quick, you can begin creating visualizations. You create a pie chart with the Amazon Comprehend sentiment data. The pie chart shows what proportion of the reviews are positive, neutral, mixed, and negative.

**To visualize sentiment data**

1. In the Quick console, choose **Analyses** and then choose **New analysis**.

1. From **Your Data Sets**, choose the sentiment dataset `sentiment_results_final` and then choose **Create analysis**.

1. In the visual editor, in **Fields list**, choose **sentiment**.
**Note**  
The values in the **Fields list** depend on the column names you used to create the tables in Amazon Athena. If you changed the provided column names in the SQL queries, the **Fields list** names will be different than the names used in these visualization examples.

1. For **Visual types**, choose **Pie chart**.

A pie chart similar to the following with positive, neutral, mixed, and negative sections is displayed. To see the count and percentage of a section, hover over it. 

![\[Console display of sentiment pie chart with sections positive, negative, neutral, and mixed.\]](http://docs.aws.amazon.com/comprehend/latest/dg/images/tutorial-reviews-pie.png)


## Create an entities visualization
<a name="tutorial-reviews-visualize-entities"></a>

Now create a second visualization with the entities dataset. You create a tree map of the distinct entities in the data. Each block in the tree map represents an entity, and the size of the block correlates to the number of times that the entity appears in the dataset.

**To visualize entities data**

1. In the **Visualize** control pane, next to **Data set**, choose the **Add, edit, replace, and remove data sets** icon.

1. Choose **Add data set**.

1. For **Choose data set to add**, choose your entities dataset `entities_results_final` from the list of datasets and choose **Select**.

1. In the **Visualize** control pane, choose the **Data set** drop down menu and choose the entities dataset `entities_results_final`.

1. In **Fields list**, choose **entity**.

1. For **Visual types**, choose **Tree map**.

A tree map similar to the following is displayed next to your pie chart. To see the count of a specific entity, hover over a block.

![\[Console display of a tree map with blocks for each unique entity.\]](http://docs.aws.amazon.com/comprehend/latest/dg/images/tutorial-reviews-tree.png)


## Publish a dashboard
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After creating the visualizations, you can publish them as a dashboard. You can perform various tasks with a dashboard, such as sharing it with users in your AWS account, saving it as a PDF, or emailing it as a report (limited to the Enterprise edition of Quick). In this step, you publish the visualizations as a dashboard in your account.

**To publish your dashboard**

1. In the navigation bar, choose **Share**.

1. Choose **Publish dashboard**.

1. Choose **Publish new dashboard as** and enter the name `comprehend-analysis-reviews` for the dashboard.

1. Choose **Publish dashboard**.

1. Close the **Share dashboard with users** pane by choosing the close button in the upper-right corner.

1. In the Quick console, in the navigation pane, choose **Dashboards**. A thumbnail of your new dashboard `comprehend-analysis-reviews` should appear under **Dashboards**. Choose the dashboard to view it.

You now have a dashboard with sentiment and entities visualizations that looks similar to the following example.

![\[Console display of a QuickSight dashboard with a pie chart and a tree map.\]](http://docs.aws.amazon.com/comprehend/latest/dg/images/tutorial-reviews-dashboard.png)


**Tip**  
 If you want to edit the visualizations in your dashboard, return to **Analyses** and edit the visualization that you want to update. Then, publish the dashboard again either as a new dashboard or as a replacement of the existing dashboard. 

## Clean up
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After completing this tutorial, you might want to clean up any AWS resources you no longer want to use. Active AWS resources can continue to incur charges in your account.

The following actions can help prevent incurring ongoing charges:
+ Cancel your Quick subscription. Quick is a monthly subscription service. To cancel your subscription, see [Canceling your subscription](https://docs.aws.amazon.com/quicksight/latest/user/closing-account.html) in the *Quick User Guide*.
+ Delete your Amazon S3 bucket. Amazon S3 charges you for storage. To clean up your Amazon S3 resources, delete your bucket. For information about deleting a bucket, see [How do I delete an S3 Bucket?](https://docs.aws.amazon.com/AmazonS3/latest/user-guide/delete-bucket.html) in the *Amazon Simple Storage Service User Guide*. Make sure that you save all of your important files before deleting your bucket.
+ Clear your AWS Glue Data Catalog. The AWS Glue Data Catalog charges you monthly for storage. You can delete your databases to prevent incurring ongoing charges. For information about managing your AWS Glue Data Catalog databases, see [Working with databases on the AWS Glue console](https://docs.aws.amazon.com/glue/latest/dg/console-databases.html) in the *AWS Glue Developer Guide*. Make sure that you export your data before clearing any databases or tables.