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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.

Mphasis HyperGraf Sentiment Analyzer

By: Mphasis Latest Version: 4.1

Deep Learning based solution which predicts positive, negative and neutral sentiments from text.

Product Overview

Sentiment Analyzer expresses a positive, negative and neutral sentiment given a text like tweets, messages, emails, blogs, reviews, forum discussions, and social posts. This module uses text analysis, natural language processing, transfer learning and deep learning techniques to predict sentiment score. Mphasis HyperGraf is an Omni-channel customer 360 analytics solution.Sentiment Analyzer expresses a positive, negative and neutral sentiment given a text like tweets, messages, emails, blogs, reviews, forum discussions, and social posts. This module uses text analysis, natural language processing, transfer learning and deep learning techniques to predict sentiment score.

Key Data

Version
By Mphasis
Categories
Type Model Package
Fulfillment Methods Amazon SageMaker

Usage Information

Fulfillment Methods

Amazon SageMaker

Usage Methodology for the algorithm: 1) The input has to be a .csv file with the content in a column titled 'text' 2) The file should follow 'utf-8' encoding. 3) The input can have a maximum of 512 words.

General instructions for consuming the service on Sagemaker: 1) Access to AWS SageMaker and the model package 2) An S3 bucket to specify input/output 3) Role for AWS SageMaker to access input/output from S3

Input

Supported content types: text/csv

sample input

SNo-|--------------------Text-------------------------

  1. school ' s out in 11 days but for now ... off ...
  2. Wouldn't surprise me if we enquired.He can't ...
  3. Rib injury for Zlatan against Russia is a big...
  4. Noooooo! I was hoping to see Zlatan being Zlat...
  5. If Wenger is in Paris. Could it be for Cavani ...

Output

Content type: text/csv

sample output

--------------input-------------------------------------|- sentiment----- school ' s out in 11 days but for now ... off ... Positive Wouldn't surprise me if we enquired.He can't ... Neutral Rib injury for Zlatan against Russia is a big... Neutral Noooooo! I was hoping to see Zlatan being Zlat... Negative If Wenger is in Paris. Could it be for Cavani ... Neutral

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.csv

Substitute the following parameters:

  • "endpoint-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

Sample Notebook : https://tinyurl.com/ts6zzed Sample Input : https://tinyurl.com/y55zzos3 Sample Output: https://tinyurl.com/wpctgln

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

Mphasis HyperGraf Sentiment Analyzer

For any assistance reach out to us 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