Amazon Sagemaker
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.
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Airline Reviews Sentiment Analyzer
By:
Latest Version:
3.1
ML based solution which classifies airline reviews into positive and negative sentiment categories.
Product Overview
This solution classifies airline reviews into positive and negative sentiments. It uses text analysis, natural language processing, machine learning techniques to predict sentiment classes for airline reviews. It also generates two word clouds: one each for positive and negative sentiment reviews. It automates the manual effort to analyze airline reviews and helps generate faster actionable insights around airline services.
Key Data
Version
By
Type
Model Package
Highlights
Airline reviews sentiment analyzer takes airline reviews as input and predicts the category of each review as positive or negative. This solution uses Natural Language Processing to process the reviews, and classifies them into positive or negative sentiment categories.
Airline reviews sentiment analyzer provides sentiments for each input review. It generates two word clouds: one for positive sentiment reviews and one for negative sentiment reviews. It also provides a bar chart explaining the distribution of positive and negative sentiments over reviews.
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Pricing Information
Use this tool to estimate the software and infrastructure costs based your configuration choices. Your usage and costs might be different from this estimate. They will be reflected on your monthly AWS billing reports.
Contact us to request contract pricing for this product.
Estimating your costs
Choose your region and launch option to see the pricing details. Then, modify the estimated price by choosing different instance types.
Version
Region
Software Pricing
Model Realtime Inference$8.00/hr
running on ml.m5.xlarge
Model Batch Transform$16.00/hr
running on ml.m5.xlarge
Infrastructure PricingWith Amazon SageMaker, you pay only for what you use. Training and inference is billed by the second, with no minimum fees and no upfront commitments. Pricing within Amazon SageMaker is broken down by on-demand ML instances, ML storage, and fees for data processing in notebooks and inference instances.
Learn more about SageMaker pricing
With Amazon SageMaker, you pay only for what you use. Training and inference is billed by the second, with no minimum fees and no upfront commitments. Pricing within Amazon SageMaker is broken down by on-demand ML instances, ML storage, and fees for data processing in notebooks and inference instances.
Learn more about SageMaker pricing
SageMaker Realtime Inference$0.23/host/hr
running on ml.m5.xlarge
SageMaker Batch Transform$0.23/host/hr
running on ml.m5.xlarge
Model Realtime Inference
For model deployment as Real-time endpoint in Amazon SageMaker, the software is priced based on hourly pricing that can vary by instance type. Additional infrastructure cost, taxes or fees may apply.InstanceType | Realtime Inference/hr | |
---|---|---|
ml.m4.4xlarge | $8.00 | |
ml.m5.4xlarge | $8.00 | |
ml.m4.16xlarge | $8.00 | |
ml.m5.2xlarge | $8.00 | |
ml.p3.16xlarge | $8.00 | |
ml.m4.2xlarge | $8.00 | |
ml.c5.2xlarge | $8.00 | |
ml.p3.2xlarge | $8.00 | |
ml.c4.2xlarge | $8.00 | |
ml.m4.10xlarge | $8.00 | |
ml.c4.xlarge | $8.00 | |
ml.m5.24xlarge | $8.00 | |
ml.c5.xlarge | $8.00 | |
ml.p2.xlarge | $8.00 | |
ml.m5.12xlarge | $8.00 | |
ml.p2.16xlarge | $8.00 | |
ml.c4.4xlarge | $8.00 | |
ml.m5.xlarge Vendor Recommended | $8.00 | |
ml.c5.9xlarge | $8.00 | |
ml.m4.xlarge | $8.00 | |
ml.c5.4xlarge | $8.00 | |
ml.p3.8xlarge | $8.00 | |
ml.m5.large | $8.00 | |
ml.c4.8xlarge | $8.00 | |
ml.p2.8xlarge | $8.00 | |
ml.c5.18xlarge | $8.00 |
Usage Information
Model input and output details
Input
Summary
- The input dataset should be in csv format.
- The column names in input file should be:
- text: airline reviews only in English Language.
- input file should not contain more than 20 reviews.
Input MIME type
text/csvSample input data
Output
Summary
- The output file (in zip format) contains the following files:
- 'output_df.csv': List of reviews along with predicted sentiment. two columns: text & predicted_sentiment
- 'pos.png': Word Cloud for Positive Sentiments.
- 'neg.png': Word Cloud for Negative Sentiments.
- 'bar.png': Distribution of Sentiments among reviews
Output MIME type
application/zipSample output data
Sample notebook
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
Airline Reviews Sentiment Analyzer
For any assistance reach out to us at:
AWS Infrastructure
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