Amazon Sagemaker
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.
HyperGraf Text Emotion Analysis
By:
Latest Version:
4.3
This solution assesses a corpus of text to predict the following emotion classes: Anger, Fear, Joy, and Sadness.
Product Overview
Emotion Analysis algorithm uses Natural Language Processing (NLP) to predict the emotion classes from a corpus of text. The algorithm analyses individual text expressions in tweets, comments, etc. made on social media platforms or captured in any other text format to classify across four emotion classes (Anger, Fear, Joy and Sadness). This can be applied to a variety of fields like marketing, medicine, banking, etc. to provide hyper personalized, tailor-made experiences to individuals.
Key Data
Version
By
Type
Model Package
Highlights
The algorithm analyses individual text expressions in tweets, comments, etc. made on social media platforms or captured in any other text format to classify in the 4 emotion classes (Anger, Fear, Joy and Sadness)
The emotion classification can be utilized for various purposes, ranging from voice of the customers on product, reaction on social media campaigns, customer service quality measurement to customer satisfaction surveys (such as NPS).
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Pricing Information
Use this tool to estimate the software and infrastructure costs based your configuration choices. Your usage and costs might be different from this estimate. They will be reflected on your monthly AWS billing reports.
Contact us to request contract pricing for this product.
Estimating your costs
Choose your region and launch option to see the pricing details. Then, modify the estimated price by choosing different instance types.
Version
Region
Software Pricing
Model Realtime Inference$4.00/hr
running on ml.m5.large
Model Batch Transform$8.00/hr
running on ml.m5.large
Infrastructure PricingWith Amazon SageMaker, you pay only for what you use. Training and inference is billed by the second, with no minimum fees and no upfront commitments. Pricing within Amazon SageMaker is broken down by on-demand ML instances, ML storage, and fees for data processing in notebooks and inference instances.
Learn more about SageMaker pricing
With Amazon SageMaker, you pay only for what you use. Training and inference is billed by the second, with no minimum fees and no upfront commitments. Pricing within Amazon SageMaker is broken down by on-demand ML instances, ML storage, and fees for data processing in notebooks and inference instances.
Learn more about SageMaker pricing
SageMaker Realtime Inference$0.115/host/hr
running on ml.m5.large
SageMaker Batch Transform$0.115/host/hr
running on ml.m5.large
Model Realtime Inference
For model deployment as Real-time endpoint in Amazon SageMaker, the software is priced based on hourly pricing that can vary by instance type. Additional infrastructure cost, taxes or fees may apply.InstanceType | Realtime Inference/hr | |
|---|---|---|
ml.m4.4xlarge | $4.00 | |
ml.m5.4xlarge | $4.00 | |
ml.m4.16xlarge | $4.00 | |
ml.m5.2xlarge | $4.00 | |
ml.p3.16xlarge | $4.00 | |
ml.m4.2xlarge | $4.00 | |
ml.c5.2xlarge | $4.00 | |
ml.p3.2xlarge | $4.00 | |
ml.c4.2xlarge | $4.00 | |
ml.m4.10xlarge | $4.00 | |
ml.c4.xlarge | $4.00 | |
ml.m5.24xlarge | $4.00 | |
ml.c5.xlarge | $4.00 | |
ml.p2.xlarge | $4.00 | |
ml.m5.12xlarge | $4.00 | |
ml.p2.16xlarge | $4.00 | |
ml.c4.4xlarge | $4.00 | |
ml.m5.xlarge | $4.00 | |
ml.c5.9xlarge | $4.00 | |
ml.m4.xlarge | $4.00 | |
ml.c5.4xlarge | $4.00 | |
ml.p3.8xlarge | $4.00 | |
ml.m5.large Vendor Recommended | $4.00 | |
ml.c4.8xlarge | $4.00 | |
ml.p2.8xlarge | $4.00 | |
ml.c5.18xlarge | $4.00 |
Usage Information
Model input and output details
Input
Summary
- The algorithm works with any text data which could be in the form of a tweet, or any other text expression by the individual
- The input must be in ‘.csv’ format
- The column containing the text data must be given the heading as “Text”
Input MIME type
text/csvSample input data
Output
Summary
Output file will contain the original text along with predicted class for each text – Anger, Fear, Joy or Sadness
Output is in the form of a ‘.csv’ file
Output MIME type
text/csv, text/plainSample output data
Sample notebook
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
HyperGraf Text Emotion Analysis
For any assistance, please reach out at:
AWS Infrastructure
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Learn MoreRefund Policy
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