Getting Started with Sentiment Analysis

sentiment-analysis-test.py

from mturk_crowd_beta_client import MTurkCrowdClient
from boto3.session import Session
import uuid

# This examples assume you have a local AWS profile called
# 'mturk-crowd-caller', but you can authenticate however you like,
# including by directly passing in your access key and secret key.
session = Session(profile_name='mturk-crowd-caller')

# Create the client
crowd_client = MTurkCrowdClient(session)

# For this example, we'll give our task a random, unique name. For prod
# tasks, you'll probably want to pick a name based on your input source.
task_name = 'my-test-task-' + uuid.uuid4().hex

# Next, we specify the name of the function to call
# In this example, we're first calling the sentiment-analysis-test function.
# The test function doesn't cost any money and is useful for validating that your account is setup correctly and for testing your integration.
# To call the prod sentiment-analysis function, uncomment the next line and comment out the sentiment-analysis-test line
# function_name = 'sentiment-analysis'
function_name = 'sentiment-analysis-test'

# The text we want labeled
text = 'Everything is wonderful.'

# Create the task              
put_result = crowd_client.put_task(function_name,
                                   task_name,
                                   {'text': text})

print('PUT response: {}'.format(
    {'status_code': put_result.status_code, 'task': put_result.json()}))

           
# Get the task we just created. Note that for a prod (i.e., non-test) task,
# we'd have to poll periodically until the task completed.
# Since we are running a test, this will return mock results. 
get_result = crowd_client.get_task(function_name, task_name)

print('GET response: {}'.format(
{'api-name': function_name, 'note': 'SAMPLE RESULTS ONLY', 'status_code': get_result.status_code, 'task': get_result.json()}))