Sold by: Mphasis
Deployed on AWS
This solution assesses a corpus of text to predict the following emotion classes: Anger, Fear, Joy, and Sadness.
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.
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).
- 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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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 | $8.00 |
ml.m5.large Inference (Real-Time) Recommended | Model inference on the ml.m5.large instance type, real-time mode | $4.00 |
ml.m4.4xlarge Inference (Batch) | Model inference on the ml.m4.4xlarge instance type, batch mode | $8.00 |
ml.m5.4xlarge Inference (Batch) | Model inference on the ml.m5.4xlarge instance type, batch mode | $8.00 |
ml.m4.16xlarge Inference (Batch) | Model inference on the ml.m4.16xlarge instance type, batch mode | $8.00 |
ml.m5.2xlarge Inference (Batch) | Model inference on the ml.m5.2xlarge instance type, batch mode | $8.00 |
ml.p3.16xlarge Inference (Batch) | Model inference on the ml.p3.16xlarge instance type, batch mode | $8.00 |
ml.m4.2xlarge Inference (Batch) | Model inference on the ml.m4.2xlarge instance type, batch mode | $8.00 |
ml.c5.2xlarge Inference (Batch) | Model inference on the ml.c5.2xlarge instance type, batch mode | $8.00 |
ml.p3.2xlarge Inference (Batch) | Model inference on the ml.p3.2xlarge instance type, batch mode | $8.00 |
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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 updates
Additional details
Inputs
- 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/csv, text/plain
https://github.com/Mphasis-ML-Marketplace/HyperGraf-Text-Emotion-Analysis/tree/main/Input
https://github.com/Mphasis-ML-Marketplace/HyperGraf-Text-Emotion-Analysis/tree/main/Input
Input data descriptions
The following table describes supported input data fields for real-time inference and batch transform.
Field name | Description | Constraints | Required |
|---|---|---|---|
Text | This column contains the textual input from which emotions needs to be extracted. | Type: FreeText | Yes |
Resources
Vendor resources
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