MachineLearningSamples-SentimentAnalysis

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MachineLearningSamples-SentimentAnalysis

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MachineLearningSamples-SentimentAnalysis

MachineLearningSamples-SentimentAnalysis 최신버젼 다운로드

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# Sentiment analysis using deep learning with Azure Machine Learning

> **NOTE** This content is no longer maintained. Visit the [Azure Machine Learning Notebook](https://github.com/Azure/MachineLearningNotebooks) project for sample Jupyter notebooks for ML and deep learning with Azure Machine Learning.

## Link to the Microsoft DOCS site

The detailed documentation for this sentiment analysis example includes the step-by-step walk-through:
[https://docs.microsoft.com/azure/machine-learning/preview/scenario-sentiment-analysis-deep-learning](https://docs.microsoft.com/azure/machine-learning/preview/scenario-sentiment-analysis-deep-learning)

## Link to the Gallery GitHub repository

The public GitHub repository for this sentiment analysis example contains all the code samples:
[https://github.com/Azure/MachineLearningSamples-SentimentAnalysis](https://github.com/Azure/MachineLearningSamples-SentimentAnalysis)

## Overview

Sentiment analysis is a well-known task in the realm of natural language processing. Given a set of texts, the objective is to determine the sentiment of that text. This example demonstrates the use of Keras to perform sentiment analysis from movie reviews.

## Key components needed to run this example

1. An [Azure account](https://azure.microsoft.com/free/) (free trials are available).
2. An installed copy of Azure Machine Learning Workbench with a workspace created.
3. The deployment part of this example is run on a Linux DSVM.

## Data / Telemetry
Sentiment Analysis collects usage data and sends it to Microsoft to help improve our products and services. Read our [privacy statement](https://privacy.microsoft.com/en-us/privacystatement) to learn more.

## Contributing

This project welcomes contributions and suggestions.  Most contributions require you to agree to a
Contributor License Agreement (CLA) declaring that you have the right to, and actually do, grant us
the rights to use your contribution. For details, visit https://cla.microsoft.com.

When you submit a pull request, a CLA-bot will automatically determine whether you need to provide
a CLA and decorate the PR appropriately (for example, label, comment). Simply follow the instructions
provided by the bot. You will only need to do this once across all repos using our CLA.

This project has adopted the [Microsoft Open Source Code of Conduct](https://opensource.microsoft.com/codeofconduct/).
For more information, see the [Code of Conduct FAQ](https://opensource.microsoft.com/codeofconduct/faq/) or
contact [opencode@microsoft.com](mailto:opencode@microsoft.com) with any additional questions or comments.

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