Sold by: ElectrifAi LLCÂ
Deployed on AWS
Breakdown and analyze sentiment of every aspect present within a text document
Overview
This algorithm takes sentiment analysis a step further by extracting each aspect and opinion within any text document. As opposed to producing sentiment scores on the entirety of a sentence, paragraph, or document, this algorithm identifies the entity being discussed, the aspect of that entity, and the opinion being discussed about that aspect. This granular approach provides a more in-depth view into customer sentiment that other models overlook.
Our machine learning models are available through a Private Offer. Please contact info@electrifai.net for subscription service pricing.
SKU: OPMSA-PS-TAT-AWS-001
Highlights
- Take an in-depth view into text documents to quickly find pertinent information needed to react responsively to customers.
Details
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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.m5.2xlarge Inference (Batch) Recommended | Model inference on the ml.m5.2xlarge instance type, batch mode | $900.00 |
ml.p2.16xlarge Inference (Real-Time) Recommended | Model inference on the ml.p2.16xlarge instance type, real-time mode | $700.00 |
ml.m5.4xlarge Inference (Batch) | Model inference on the ml.m5.4xlarge instance type, batch mode | $500.00 |
ml.m5.large Inference (Batch) | Model inference on the ml.m5.large instance type, batch mode | $700.00 |
ml.p2.xlarge Inference (Real-Time) | Model inference on the ml.p2.xlarge instance type, real-time mode | $500.00 |
ml.p3.16xlarge Inference (Real-Time) | Model inference on the ml.p3.16xlarge instance type, real-time mode | $900.00 |
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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
Vulnerability CVE-2021-3177 (i.e. https://nvd.nist.gov/vuln/detail/CVE-2021-3177 ) has been resolved in version 1.0.1.
Additional details
Inputs
- Summary
JSON content like { "content": "The park was very fun. It was an excellent vacation." }
- Input MIME type
- application/json
https://github.com/ElectrifAi/model-aws-opinion-mining-sentiment-analysis/blob/master/input.json
https://github.com/ElectrifAi/model-aws-opinion-mining-sentiment-analysis/blob/master/input.json
Input data descriptions
The following table describes supported input data fields for real-time inference and batch transform.
Field name | Description | Constraints | Required |
|---|---|---|---|
Json input data: "content": "The park was very fun. It was an excellent vacation." | {
"content": "The park was very fun. It was an excellent vacation."
} | Type: FreeText | Yes |
Resources
Vendor resources
Support
AWS infrastructure support
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This is a large text document with around 38,000 Judicial Opinions with a file date within the year 2022. Opinions were selected from state and federal courts. These opinions are each at least 100 characters in length. Rows contain the date filed, case name, court name, and the text of the opinion. This is a csv formatted file. The size is approximately 750 MB.
This is a large text document with around 1 Million Judicial Opinions. Opinions were randomly selected from local, state, and federal courts. These opinions are each at least 100 characters in length. Each line contains the case name, typically the judge name, and then the opinion text. Each opinion is separated by a new line. Total file size is around 10 GB.
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