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Commit e8a10bf

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jerjouJon Wayne Parrott
jerjou
authored and
Jon Wayne Parrott
committed
Add sentiment analysis sample (GoogleCloudPlatform#533)
* Add sentiment analysis sample * Move sample review files into resources directory * Remove blank line from end of file * Update set up instructions to point to getting started guide * Update README to remove need to set up gcloud. Itemize what setting up a project entails. * Update NL README to link to Sentiment tutorial code * Coerce number types before comparison
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‎language/README.md

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@@ -13,5 +13,9 @@ to extract text from images, then uses the NL API to extract entity information
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from those texts, and stores the extracted information in a database in support
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of further analysis and correlation.
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- [sentiment](sentiment) contains the [Sentiment Analysis
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Tutorial](https://cloud.google.com/natural-language/docs/sentiment-tutorial)
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code as used within the documentation.
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- [syntax_triples](syntax_triples) uses syntax analysis to find
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subject-verb-object triples in a given piece of text.

‎language/sentiment/README.md

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# Introduction
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This sample contains the code referenced in the
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[Sentiment Analysis Tutorial](http://cloud.google.com/natural-language/docs/sentiment-tutorial)
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within the Google Cloud Natural Language API Documentation. A full walkthrough of this sample
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is located within the documentation.
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This sample is a simple illustration of how to construct a sentiment analysis
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request and process a response using the API.
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## Prerequisites
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Set up your
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[Cloud Natural Language API project](https://cloud.google.com/natural-language/docs/getting-started#set_up_a_project)
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, which includes:
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* Enabling the Natural Language API
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* Setting up a service account
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* Ensuring you've properly set up your `GOOGLE_APPLICATION_CREDENTIALS` for proper
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authentication to the service.
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## Download the Code
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```
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$ git clone https://github.com/GoogleCloudPlatform/python-dev-samples/language/sentiment/
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$ cd python-docs-samples/language/sentiment
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```
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## Run the Code
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Open a sample folder, create a virtualenv, install dependencies, and run the sample:
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```
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$ virtualenv env
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$ source env/bin/activate
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(env)$ pip install -r requirements.txt
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```
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### Usage
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This sample provides four sample movie reviews which you can
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provide to the sample on the command line. (You can also
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pass your own text files.)
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```
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(env)$ python sentiment_analysis.py textfile.txt
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Sentiment: polarity of -0.1 with magnitude of 6.7
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```

‎language/sentiment/requirements.txt

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google-api-python-client==1.5.3
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‎language/sentiment/resources/mixed.txt

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I really wanted to love 'Bladerunner' but ultimately I couldn't get
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myself to appreciate it fully. However, you may like it if you're into
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science fiction, especially if you're interested in the philosophical
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exploration of what it means to be human or machine. Some of the gizmos
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like the flying cars and the Vouight-Kampff machine (which seemed very
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steampunk), were quite cool.
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I did find the plot pretty slow and but the dialogue and action sequences
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were good. Unlike most science fiction films, this one was mostly quiet, and
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not all that much happened, except during the last 15 minutes. I didn't
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understand why a unicorn was in the movie. The visual effects were fantastic,
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however, and the musical score and overall mood was quite interesting.
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A futurist Los Angeles that was both highly polished and also falling apart
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reminded me of 'Outland.' Certainly, the style of the film made up for
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many of its pedantic plot holes.
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If you want your sci-fi to be lasers and spaceships, 'Bladerunner' may
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disappoint you. But if you want it to make you think, this movie may
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be worth the money.
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‎language/sentiment/resources/neg.txt

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What was Hollywood thinking with this movie! I hated,
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hated, hated it. BORING! I went afterwards and demanded my money back.
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They refused.
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‎language/sentiment/resources/neutral.txt

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I neither liked nor disliked this movie. Parts were interesting, but
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overall I was left wanting more. The acting was pretty good.
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‎language/sentiment/resources/pos.txt

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`Bladerunner` is often touted as one of the best science fiction films ever
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made. Indeed, it satisfies many of the requisites for good sci-fi: a future
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world with flying cars and humanoid robots attempting to rebel against their
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creators. But more than anything, `Bladerunner` is a fantastic exploration
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of the nature of what it means to be human. If we create robots which can
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think, will they become human? And if they do, what makes us unique? Indeed,
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how can we be sure we're not human in any case? `Bladerunner` explored
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these issues before such movies as `The Matrix,' and did so intelligently.
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The visual effects and score by Vangelis set the mood. See this movie
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in a dark theatre to appreciate it fully. Highly recommended!
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‎language/sentiment/sentiment_analysis.py

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# Copyright 2016, Google, Inc.
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# Licensed under the Apache License, Version 2.0 (the "License");
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# you may not use this file except in compliance with the License.
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# You may obtain a copy of the License at
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#
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# http://www.apache.org/licenses/LICENSE-2.0
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#
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# Unless required by applicable law or agreed to in writing, software
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# distributed under the License is distributed on an "AS IS" BASIS,
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# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
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# See the License for the specific language governing permissions and
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# limitations under the License.
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'''Demonstrates how to make a simple call to the Natural Language API'''
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import argparse
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from googleapiclient import discovery
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from oauth2client.client import GoogleCredentials
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def main(movie_review_filename):
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'''Run a sentiment analysis request on text within a passed filename.'''
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credentials = GoogleCredentials.get_application_default()
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service = discovery.build('language', 'v1beta1', credentials=credentials)
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with open(movie_review_filename, 'r') as review_file:
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service_request = service.documents().analyzeSentiment(
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body={
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'document': {
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'type': 'PLAIN_TEXT',
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'content': review_file.read(),
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}
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}
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)
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response = service_request.execute()
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polarity = response['documentSentiment']['polarity']
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magnitude = response['documentSentiment']['magnitude']
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print('Sentiment: polarity of {} with magnitude of {}'.format(
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polarity, magnitude))
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return 0
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if __name__ == '__main__':
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parser = argparse.ArgumentParser(
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description=__doc__,
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formatter_class=argparse.RawDescriptionHelpFormatter)
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parser.add_argument(
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'movie_review_filename',
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help='The filename of the movie review you\'d like to analyze.')
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args = parser.parse_args()
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main(args.movie_review_filename)

‎language/sentiment/sentiment_analysis_test.py

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# Copyright 2016, Google, Inc.
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# Licensed under the Apache License, Version 2.0 (the "License");
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# you may not use this file except in compliance with the License.
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# You may obtain a copy of the License at
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#
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# http://www.apache.org/licenses/LICENSE-2.0
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#
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# Unless required by applicable law or agreed to in writing, software
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# distributed under the License is distributed on an "AS IS" BASIS,
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# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
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# See the License for the specific language governing permissions and
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# limitations under the License.
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import re
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from sentiment_analysis import main
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def test_pos(resource, capsys):
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main(resource('pos.txt'))
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out, err = capsys.readouterr()
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polarity = float(re.search('polarity of (.+?) with', out).group(1))
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magnitude = float(re.search('magnitude of (.+?)', out).group(1))
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assert polarity * magnitude > 0
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def test_neg(resource, capsys):
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main(resource('neg.txt'))
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out, err = capsys.readouterr()
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polarity = float(re.search('polarity of (.+?) with', out).group(1))
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magnitude = float(re.search('magnitude of (.+?)', out).group(1))
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assert polarity * magnitude < 0
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def test_mixed(resource, capsys):
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main(resource('mixed.txt'))
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out, err = capsys.readouterr()
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polarity = float(re.search('polarity of (.+?) with', out).group(1))
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assert polarity <= 0.3
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assert polarity >= -0.3
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def test_neutral(resource, capsys):
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main(resource('neutral.txt'))
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out, err = capsys.readouterr()
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magnitude = float(re.search('magnitude of (.+?)', out).group(1))
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assert magnitude <= 2.0

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