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Amazon SageMaker is a fully-managed platform that enables developers and data scientists to quickly and easily build, train, and deploy machine learning models at any scale. With Amazon SageMaker, all the barriers and complexity that typically slow down developers who want to use machine learning are removed. The service includes models that can be used together or independently to build, train, and deploy your machine learning models.

Assess Corp. Values from Employee Review

By: Mphasis Latest Version: 3.2

Lexicon based NLP Solution analyses employee reviews to assess which cultural values are being mentioned.

Product Overview

Corporate culture defined as "a set of norms and values that are widely shared and strongly held throughout the organization" is an important aspect of employees’ relationship with their organization. The most important corporate cultural categories are: Integrity, Teamwork, Innovation, Respect, Quality, Safety, Community, Communication, and Reward. This solution identifies which of these 9 values find mention in employee reviews. This enables organizations to assess whether the values they espouse are currently experienced by their employees in the workplace, and track changes in corporate values over time.

Key Data

Version
By Mphasis
Categories
Type Model Package
Fulfillment Methods Amazon SageMaker

Usage Information

Fulfillment Methods

Amazon SageMaker

Amazon SageMaker

Input

• Supported content types: text/csv • Sample input file: (https://tinyurl.com/y42hr3bg) • The input can be provided as (.csv) file and with 'utf-8' encoding • The input file should contain only a single column heading as 'Reviews', with different rows containing different reviews •The solution currently handles only English language reviews with a maximum of reviews 9000 per request (~800kb)

Output

• Content type: text/csv • Output file will contain the original 'Reviews' column along with various cultural category columns • Each of the cultural category column can have values either 'Yes' or 'No' • Sample output file: (https://tinyurl.com/yyln5ehv)

Invoking endpoint

AWS CLI Command

If you are using real time inferencing, please create the endpoint first and then use the following command to invoke it:

aws sagemaker-runtime invoke-endpoint --endpoint-name "endpoint-name" --body fileb://cultural_categ_input.csv --content-type text/csv --accept application/cultural_categ_output.csv

Substitute the following parameters:

  • "endpoint-name" - name of the inference endpoint where the model is deployed

  • cultural_categ_input.csv - input csv file to do the inference on

  • text/csv - type of the given input text file

  • cultural_categ_output.csv - filename where the inference results are written to

    Resources

    • [Cultural category sample data] (https://tinyurl.com/y42hr3bg) • [Sample Notebook] (https://tinyurl.com/y3amtsrp)

Additional Resources

End User License Agreement

By subscribing to this product you agree to terms and conditions outlined in the product End User License Agreement (EULA)

Support Information

Assess Corp. Values from Employee Review

For any assistance, please reach out at:

https://www2.mphasis.com/AWS-Marketplace-Support-LP.html

AWS Infrastructure

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Refund Policy

Currently we do not support refunds, but you can cancel your subscription to the service at any time.

Customer Reviews