Sold by: MphasisÂ
Deployed on AWS
This solution predicts sentiments for employee reviews on 5 aspects - career opp, comp & ben, work life balance, work culture & leadership.
Overview
Aspect Based Employee Sentiment Analyzer identifies positive or negative sentiments in employee reviews for specific aspects. The aspects include career opportunities, compensation and benefits, work life balance, work culture, and leadership. It automates the manual effort to analyze employee feedback surveys and helps generate faster actionable insights around specific aspects.
Highlights
- Aspect Based Employee Sentiment Analyzer identifies sentiments in employee reviews towards a specific aspect. This model uses lexical, syntactic & semantic understanding of input text, with a combination of linguistics and deep learning methods with respect to the given aspects for sentiment prediction. This helps users to categorize the reviews they receive and conduct a detailed analysis of individual categories.
- State-of-the-Art Transformer based models that capture context and helps in Sentiment Analysis of aspect driven reviews.
- Mphasis HyperGraf is an omni-channel customer 360 analytics solution. Need customized Deep Learning/NLP solutions? Get in touch!
Details
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Delivery method
Latest version
Deployed on AWS
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Features and programs
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Pricing
Pricing is based on actual usage, with charges varying according to how much you consume. Subscriptions have no end date and may be canceled any time.
Additional AWS infrastructure costs may apply. Use the AWS Pricing Calculator to estimate your infrastructure costs.
Dimension | Description | Cost/host/hour |
|---|---|---|
ml.m5.large Inference (Batch) Recommended | Model inference on the ml.m5.large instance type, batch mode | $16.00 |
ml.m5.large Inference (Real-Time) Recommended | Model inference on the ml.m5.large instance type, real-time mode | $8.00 |
ml.m4.4xlarge Inference (Batch) | Model inference on the ml.m4.4xlarge instance type, batch mode | $16.00 |
ml.m5.4xlarge Inference (Batch) | Model inference on the ml.m5.4xlarge instance type, batch mode | $16.00 |
ml.m4.16xlarge Inference (Batch) | Model inference on the ml.m4.16xlarge instance type, batch mode | $16.00 |
ml.m5.2xlarge Inference (Batch) | Model inference on the ml.m5.2xlarge instance type, batch mode | $16.00 |
ml.p3.16xlarge Inference (Batch) | Model inference on the ml.p3.16xlarge instance type, batch mode | $16.00 |
ml.m4.2xlarge Inference (Batch) | Model inference on the ml.m4.2xlarge instance type, batch mode | $16.00 |
ml.c5.2xlarge Inference (Batch) | Model inference on the ml.c5.2xlarge instance type, batch mode | $16.00 |
ml.p3.2xlarge Inference (Batch) | Model inference on the ml.p3.2xlarge instance type, batch mode | $16.00 |
Vendor refund policy
Currently we do not support refunds, but you can cancel your subscription to the service at any time.
Legal
Vendor terms and conditions
Upon subscribing to this product, you must acknowledge and agree to the terms and conditions outlined in the vendor's End User License Agreement (EULA)Â .
Content disclaimer
Vendors are responsible for their product descriptions and other product content. AWS does not warrant that vendors' product descriptions or other product content are accurate, complete, reliable, current, or error-free.
Delivery details
Amazon SageMaker model
An Amazon SageMaker model package is a pre-trained machine learning model ready to use without additional training. Use the model package to create a model on Amazon SageMaker for real-time inference or batch processing. Amazon SageMaker is a fully managed platform for building, training, and deploying machine learning models at scale.
Deploy the model on Amazon SageMaker AI using the following options:
Version release notes
Bug Fixes and Performance Improvement
Additional details
Inputs
- Summary
Usage Methodology for the algorithm:
- The input must be 'csv' file.
- The reviews should be placed in review column and the aspects should be placed in aspect column in the input file.
- The file should follow 'utf-8' encoding.
- The input file should not exceed 50kb ~100 reviews.
General instructions for consuming the service on Sagemaker:
- Access to AWS SageMaker and the model package
- An S3 bucket to specify input/output
- Role for AWS SageMaker to access input/output from S3
Input
Supported content types: text/csv
sample input
|--------------------review-----------------------------|-----------aspect----------| | I love my work, I have a lot of opportunities-----|---career opportunties---|
Aspects in the solution:
- career opportunities
- comepensation and benefits
- work life balance
- culture and values
- senior management
Output
Content type: text/csv
sample output
|--sentiment--|-----------aspect----------|--------------------review-----------------------------| |---Positive----|---career opportunties---| I love my work, I have a lot of opportunities-----|
Invoking endpoint
AWS CLI Command
You can invoke endpoint using AWS CLI:
aws sagemaker-runtime invoke-endpoint --endpoint-name $model_name --body fileb://$file_name --content-type 'text/csv' --region us-east-2 output.csvSubstitute the following parameters:
- $model_name - name of the inference endpoint where the model is deployed
- input.csv - input file to do the inference on
- text/csv - Type of input data
- output.csv - filename where the inference results are written to
Resources
- Input MIME type
- text/csv, text/plain
See Input Summary
See Input Summary
Resources
Vendor resources
Support
Vendor support
For any assistance reach out to us at:
AWS infrastructure support
AWS Support is a one-on-one, fast-response support channel that is staffed 24x7x365 with experienced and technical support engineers. The service helps customers of all sizes and technical abilities to successfully utilize the products and features provided by Amazon Web Services.
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