Sold by: Mphasis
Deployed on AWS
A Natural Language Processing based method to identify and classify topics from mobile application reviews.
Overview
The solution categorizes mobile application reviews into four prominent categories based on user reviews: User Experience, Safety and Security, Functional stability, and Ease of use. Deep learning based simple transformer learners are used to classify the review into one of the above categories.
Highlights
- A Deep Learning solution with Transformer based learners predicts the category of reviews based on the description provided by the user. The prediction engine comprises a pre-trained model to predict the topics of review category. Since the model is pre-built, there is no need to train.
- The set of review categories used by the Transformer learners are observed to be prominent in mobile application domain and are derived from large corpus of textual description using NLP/ML based approaches.
- Mphasis HyperGraf is an omni-channel customer 360 analytics solution. Need customized Machine Learning and Deep Learning 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 | $16.00 |
ml.m5.xlarge Inference (Real-Time) Recommended | Model inference on the ml.m5.xlarge instance type, real-time mode | $8.00 |
ml.m4.4xlarge Inference (Batch) | Model inference on the ml.m4.4xlarge instance type, batch mode | $16.00 |
ml.m5.4xlarge Inference (Batch) | Model inference on the ml.m5.4xlarge instance type, batch mode | $16.00 |
ml.m4.16xlarge Inference (Batch) | Model inference on the ml.m4.16xlarge instance type, batch mode | $16.00 |
ml.m5.2xlarge Inference (Batch) | Model inference on the ml.m5.2xlarge instance type, batch mode | $16.00 |
ml.p3.16xlarge Inference (Batch) | Model inference on the ml.p3.16xlarge instance type, batch mode | $16.00 |
ml.m4.2xlarge Inference (Batch) | Model inference on the ml.m4.2xlarge instance type, batch mode | $16.00 |
ml.c5.2xlarge Inference (Batch) | Model inference on the ml.c5.2xlarge instance type, batch mode | $16.00 |
ml.p3.2xlarge Inference (Batch) | Model inference on the ml.p3.2xlarge instance type, batch mode | $16.00 |
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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
It is 3.1 version of the solution.
Additional details
Inputs
- Summary
The input file should be in csv format with two columns namely, ID and Review. ID: Unique ID for each review Review: Textual review data
- Input MIME type
- text/csv
https://github.com/Mphasis-ML-Marketplace/Topic-Extraction-from-Mobile-App-Reviews/blob/main/input/sample_input.csv
https://github.com/Mphasis-ML-Marketplace/Topic-Extraction-from-Mobile-App-Reviews/blob/main/input/sample_input.csv
Input data descriptions
The following table describes supported input data fields for real-time inference and batch transform.
Field name | Description | Constraints | Required |
|---|---|---|---|
ID | ID for each review. | Type: Categorical
Allowed values: Eg:1,2,3,.. | Yes |
Review | Textual description of review. | Type: FreeText | Yes |
Resources
Vendor resources
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