https://github.com/cloudacademy/sentiment-analysis-aws-lambda

How to deploy a Machine Learning model for sentiment analysis in the Cloud with AWS Lambda.
https://github.com/cloudacademy/sentiment-analysis-aws-lambda

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How to deploy a Machine Learning model for sentiment analysis in the Cloud with AWS Lambda.

• Host: GitHub
• URL: https://github.com/cloudacademy/sentiment-analysis-aws-lambda
• Owner: cloudacademy
• Created: 2016-04-22T11:00:32.000Z (over 8 years ago)
• Default Branch: master
• Last Pushed: 2020-10-22T10:15:09.000Z (about 4 years ago)
• Last Synced: 2024-08-01T06:21:42.637Z (3 months ago)
• Language: Python
• Size: 37.3 MB
• Stars: 106
• Watchers: 21
• Forks: 31
• Open Issues: 0
• Metadata Files:
  • Readme: README.md

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README

        
# Sentiment analysis in the Cloud with AWS Lambda.

The Cloud Academy team shows how to build a sentiment analysis machine learning model by using a pubic dataset and how to deploy it to production with AWS Lambda and API Gateway.

![Frontend Screenshot](frontend/screenshot.png?raw=true)

## How to install requirements

OS libraries can be installed as follows (Linux):

    $ sudo yum install -y atlas-devel atlas-sse3-devel blas-devel gcc gcc-c++ lapack-devel python27-devel

Python requirements can be installed with pip.

    $ pip install -r requirements.txt

    

## Training phase

You can train and persist the model into file by executing:

    $ python main.py

## Test the model locally

You can run the model via CLI as follows:

    $ python predict.py "This function is awesome"

    > positive

    

## How to deploy the Lambda function

Simply execute the following deployment script:

    $ ./deploy.sh

It will automatically create a new deployment package containing every Python dependency and the OS libraries required by scikit-learn and numpy, together with the "lambda" folder.

[Here](https://github.com/ryansb/sklearn-build-lambda) you can find the whole stack ready to be uploaded  ([reference](https://serverlesscode.com/post/deploy-scikitlearn-on-lamba/)).

Once the deployment package is ready, you can create a new AWS Lambda function and bind it to a new Amazon API Gateway endpoint.

### References

* [Cloud Academy & AWS: how we use Amazon Web Services for machine learning and data collection](https://cloudacademy.com/webinars/cloud-academy-and-aws-how-we-use-aws-machine-learning-and-data-collection-10/) (webinar)

* [Using Scikit-Learn In AWS Lambda](https://serverlesscode.com/post/deploy-scikitlearn-on-lamba/) (article by [Ryan Brown](https://github.com/ryansb))

* [Sentiment Analysis dataset](http://cs.stanford.edu/people/alecmgo/trainingandtestdata.zip) (by cs.stanford.edu)

* [Amazon Serverless Architecture](http://cloudacademy.com/blog/amazon-serverless-api-gateway-lambda-cloudfront-s3/) (Cloud Academy blog)

* [Google Cloud Functions VS AWS Lambda](http://cloudacademy.com/blog/google-cloud-functions-serverless/) (Cloud Academy blog)