Sold by: MphasisÂ
Deployed on AWS
This is a Natural Language Processing based solution which can detect up to 8 aspects from online product reviews for Mounts.
Overview
This solution identifies the various aspects from online product reviews for Mounts. The following 8 aspects are identified: price, build quality, assembly and installation, range of motion, load carrying, size and dimensions, warranty and, performance. This enables companies to easily identify which aspects are being reviewed. This information can be used to assess changes in consumer priorities and focus on trending aspects related to Mounts. Companies can use this solution for providing inputs to advertising and marketing campaigns and product design.
Highlights
- This solution is trained on a large publicly available dataset of Mounts reviews. Solution uses semi-supervised latent dirichlet allocation algorithm to learn the important keywords for 8 aspects related to Mounts. Aspects for new reviews are inferred on the basis of comparison of words in the reviews with the model-learnt set of words for each aspect.
- This solution can be used by e-commerce companies, manufacturers, and retailers to identify the aspects from online customer reviews.
- Mphasis HyperGraf is an Omni-channel customer 360 analytics solution.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 | $20.00 |
ml.m5.large Inference (Real-Time) Recommended | Model inference on the ml.m5.large instance type, real-time mode | $10.00 |
ml.m4.4xlarge Inference (Batch) | Model inference on the ml.m4.4xlarge instance type, batch mode | $20.00 |
ml.m5.4xlarge Inference (Batch) | Model inference on the ml.m5.4xlarge instance type, batch mode | $20.00 |
ml.m4.16xlarge Inference (Batch) | Model inference on the ml.m4.16xlarge instance type, batch mode | $20.00 |
ml.m5.2xlarge Inference (Batch) | Model inference on the ml.m5.2xlarge instance type, batch mode | $20.00 |
ml.p3.16xlarge Inference (Batch) | Model inference on the ml.p3.16xlarge instance type, batch mode | $20.00 |
ml.m4.2xlarge Inference (Batch) | Model inference on the ml.m4.2xlarge instance type, batch mode | $20.00 |
ml.c5.2xlarge Inference (Batch) | Model inference on the ml.c5.2xlarge instance type, batch mode | $20.00 |
ml.p3.2xlarge Inference (Batch) | Model inference on the ml.p3.2xlarge instance type, batch mode | $20.00 |
Vendor refund policy
Currently we do not support refunds, but you can cancel your subscription to the service at any time.
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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
Each text file should have 1 review with maximum of 2000 characters. Should have only ASCII characters. Works only for English language inputs.
- Input MIME type
- text/plain
https://github.com/Mphasis-ML-Marketplace/Aspect-Detection-Mounts-Reviews/blob/main/Input/sample.txt
https://github.com/Mphasis-ML-Marketplace/Aspect-Detection-Mounts-Reviews/blob/main/Input/sample.txt
Resources
Vendor resources
Support
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