Step 3: Running Analysis Jobs on Documents in Amazon S3 - Amazon Comprehend

Step 3: Running Analysis Jobs on Documents in Amazon S3

After storing the data in Amazon S3, you can begin running Amazon Comprehend analysis jobs. A sentiment analysis job determines the overall mood of a document (positive, negative, neutral, or mixed). An entities analysis job extracts the names of real-world objects from a document. These objects include people, places, titles, events, dates, quantities, products, and organizations. In this step, you run two Amazon Comprehend analysis jobs to extract the sentiment and entities from the sample dataset.

Prerequisites

Before you begin, do the following:

Analyze Sentiment and Entities

The first job you run analyzes the sentiment of each customer review in the sample dataset. The second job extracts the entities in each customer review. You can perform Amazon Comprehend analysis jobs either using the Amazon Comprehend console or the AWS CLI.

Tip

Make sure that you are in an AWS Region that supports Amazon Comprehend. For more information, see the Region Table in the Global Infrastructure Guide.

When using the Amazon Comprehend console, you create one job at a time. You need to repeat the following steps in order to run both a sentiment and an entities analysis job. Note that for the first job, you create an IAM role, but for the second job, you can reuse the first job's IAM role. You can reuse the IAM role as long as you use the same S3 bucket and folders.

To run sentiment and entities analysis jobs (console)

  1. Ensure that you're in the same Region in which you created your Amazon Simple Storage Service (Amazon S3) bucket. If you're in another Region, in the navigation bar, choose the AWS Region where you created your S3 bucket from the Region selector.

  2. Open the Amazon Comprehend console at https://console.aws.amazon.com/comprehend/

  3. Choose Launch Amazon Comprehend.

  4. In the navigation pane, choose Analysis jobs.

  5. Choose Create job.

  6. In the Job settings section, do the following:

    1. For Name, enter reviews-sentiment-analysis.

    2. For Analysis type, choose Sentiment.

    3. For Language, choose English.

    4. Leave Job encryption turned off.

  7. In the Input data section, do the following:

    1. For Data source, choose My documents.

    2. For S3 location, choose Browse S3 and then choose your bucket from the list of buckets.

    3. In your S3 bucket, for Objects, choose your input folder.

    4. In the input folder, choose the sample dataset amazon-reviews.csv and then choose Choose.

    5. For Input format, choose One document per line.

  8. In the Output data section, do the following:

    1. For S3 location, choose Browse S3 and then choose your bucket from the list of buckets.

    2. In your S3 bucket, for Objects, choose the output folder and then choose Choose.

    3. Leave Encryption turned off.

  9. In the Access permissions section, do the following:

    1. For IAM role, choose Create an IAM role.

    2. For Permissions to access, choose Input and Output S3 buckets.

    3. For Name suffix, enter comprehend-access-role. This role provides access to your Amazon S3 bucket.

  10. Choose Create job.

  11. Repeat steps 1-10 to create an entities analysis job. Make the following changes:

    1. In Job settings, for Name, enter reviews-entities-analysis.

    2. In Job settings, for Analysis type, choose Entities.

    3. In Access permissions, choose Use an existing IAM role. For Role name, choose AmazonComprehendServiceRole-comprehend-access-role (this is the same role you created for the sentiment job).

You use the start-sentiment-detection-job and the start-entities-detection-job commands to run sentiment and entities analysis jobs. After you run each command, the AWS CLI shows a JSON object with a JobId value that allows you to access details about the job, including the output S3 location.

To run sentiment and entities analysis jobs (AWS CLI)

  1. Start a sentiment analysis job by running the following command in the AWS CLI. Replace arn:aws:iam::123456789012:role/comprehend-access-role with the IAM role ARN that you previously copied to a text editor. If your default AWS CLI Region differs from the Region in which you created your Amazon S3 bucket, include the --region parameter and replace us-east-1 with the Region where your bucket resides.

    aws comprehend start-sentiment-detection-job --input-data-config S3Uri=s3://DOC-EXAMPLE-BUCKET/input/ --output-data-config S3Uri=s3://DOC-EXAMPLE-BUCKET/output/ --data-access-role-arn arn:aws:iam::123456789012:role/comprehend-access-role --job-name reviews-sentiment-analysis --language-code en [--region us-east-1]
  2. After you submit the job, copy the JobId and save it to a text editor. You will need the JobId to find the output files from the analysis job.

  3. Start an entities analysis job by running the following command.

    aws comprehend start-entities-detection-job --input-data-config S3Uri=s3://DOC-EXAMPLE-BUCKET/input/ --output-data-config S3Uri=s3://DOC-EXAMPLE-BUCKET/output/ --data-access-role-arn arn:aws:iam::123456789012:role/comprehend-access-role --job-name reviews-entities-analysis --language-code en [--region us-east-1]
  4. After you submit the job, copy the JobId and save it to a text editor.

  5. Check the status of your jobs. You can view the progress of a job by tracking its JobId.

    To track the progress of your sentiment analysis job, run the following command. Replace sentiment-job-id with the JobId that you copied after running your sentiment analysis.

    aws comprehend describe-sentiment-detection-job --job-id sentiment-job-id

    To track your entities analysis job, run the following command. Replace entities-job-id with the JobId that you copied after running your entities analysis.

    aws comprehend describe-entities-detection-job --job-id entities-job-id

    It takes several minutes for the JobStatus to show as COMPLETED.

You have completed sentiment and entities analysis jobs. Both of the jobs should be completed before you move on to the next step. It can take several minutes for the jobs to finish.