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added README.md sample output
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‎language/api/README.md

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# Google Cloud Natural Language API Sample
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This Python sample demonstrates the use of the [Google Cloud Natural Language API][NL-Docs]
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for sentiment, entity, and syntax analysis.
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[NL-Docs]: https://cloud.google.com/natural-language/docs/
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## Setup
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Please follow the [Set Up Your Project](https://cloud.google.com/natural-language/docs/getting-started#set_up_your_project)
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steps in the Quickstart doc to create a project and enable the
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Cloud Natural Language API. Following those steps, make sure that you
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[Set Up a Service Account](https://cloud.google.com/natural-language/docs/common/auth#set_up_a_service_account),
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and export the following environment variable:
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```
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export GOOGLE_APPLICATION_CREDENTIALS=/path/to/your-project-credentials.json
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```
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## Run the sample
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Install [pip](https://pip.pypa.io/en/stable/installing) if not already installed.
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To run the example, install the necessary libraries using pip:
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```sh
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$ pip install -r requirements.txt
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```
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Then, run the script:
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```sh
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$ python analyze.py <command> <text-string>
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```
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where `<command>` is one of: `entities`, `sentiment`, or `syntax`.
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The script will write to STDOUT the json returned from the API for the requested feature.
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* Example1:
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```sh
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$ python analyze.py entities "Tom Sawyer is a book written by a guy known as Mark Twain."
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```
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You will see something like the following returned:
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```
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{
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"entities": [
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{
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"salience": 0.50827783,
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"mentions": [
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{
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"text": {
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"content": "Tom Sawyer",
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"beginOffset": 0
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},
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"type": "PROPER"
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}
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],
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"type": "PERSON",
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"name": "Tom Sawyer",
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"metadata": {
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"mid": "/m/01b6vv",
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"wikipedia_url": "http://en.wikipedia.org/wiki/The_Adventures_of_Tom_Sawyer"
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}
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},
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{
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"salience": 0.22226454,
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"mentions": [
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{
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"text": {
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"content": "book",
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"beginOffset": 16
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},
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"type": "COMMON"
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}
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],
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"type": "WORK_OF_ART",
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"name": "book",
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"metadata": {}
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},
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{
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"salience": 0.18305534,
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"mentions": [
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{
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"text": {
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"content": "guy",
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"beginOffset": 34
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},
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"type": "COMMON"
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}
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],
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"type": "PERSON",
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"name": "guy",
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"metadata": {}
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},
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{
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"salience": 0.086402282,
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"mentions": [
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{
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"text": {
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"content": "Mark Twain",
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"beginOffset": 47
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},
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"type": "PROPER"
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}
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],
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"type": "PERSON",
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"name": "Mark Twain",
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"metadata": {
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"mid": "/m/014635",
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"wikipedia_url": "http://en.wikipedia.org/wiki/Mark_Twain"
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}
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}
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],
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"language": "en"
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}
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```
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* Example2:
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```sh
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$ python analyze.py entities "Apple has launched new iPhone."
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```
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You will see something like the following returned:
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```
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{
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"entities": [
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{
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"salience": 0.72550339,
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"mentions": [
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{
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"text": {
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"content": "Apple",
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"beginOffset": 0
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},
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"type": "PROPER"
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}
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],
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"type": "ORGANIZATION",
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"name": "Apple",
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"metadata": {
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"mid": "/m/0k8z",
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"wikipedia_url": "http://en.wikipedia.org/wiki/Apple_Inc."
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}
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},
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{
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"salience": 0.27449661,
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"mentions": [
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{
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"text": {
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"content": "iPhone",
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"beginOffset": 23
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},
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"type": "PROPER"
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}
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],
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"type": "CONSUMER_GOOD",
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"name": "iPhone",
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"metadata": {
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"mid": "/m/027lnzs",
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"wikipedia_url": "http://en.wikipedia.org/wiki/IPhone"
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}
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}
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],
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"language": "en"
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}
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```

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