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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.

HyperGraf Text Emotion Analysis

By: Mphasis Latest Version: 3.2

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. Mphasis HyperGraf is an Omni-channel customer 360 analytics solution.

Key Data

Version
By Mphasis
Categories
Type Model Package
Fulfillment Methods Amazon SageMaker

Usage Information

Fulfillment Methods

Amazon SageMaker

Input

• Supported content types: text/csv • Sample input file: ( https://tinyurl.com/teylmvw) | Text | | |------------------------------------------------|---| | "My mother was really angry with me." | |

• 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 column containing the text data must be given the heading as “Text” • File size of input data (.csv file) should be less then 550KB

Output

• Content type: text/csv • Sample output file: | TEXT | Emotion Category | |------------------------------------------------|-----------------------| | Â "My mother was really angry with me." | Anger |

• Output file will contain the original text along with predicted class for each text input- Anger, Fear, Joy or Sadness

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://input.csv --content-type text/csv --accept application/output.csv

Substitute the following parameters:

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

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

  • text/csv - MIME type of the given input image (above)

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

    Resources

    • [Sample Input] ( https://tinyurl.com/teylmvw) • [Sample Notebook] ( https://tinyurl.com/tdxb7yh)

Support Information

HyperGraf Text Emotion Analysis

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.

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