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60 lines
1.8 KiB
Python
60 lines
1.8 KiB
Python
#!/usr/bin/env python2
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# -*-coding:UTF-8 -*
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"""
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Template for new modules
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nltk.sentiment.vader module:
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Hutto, C.J. & Gilbert, E.E. (2014). VADER: A Parsimonious Rule-based Model for Sentiment Analysis of Social Media Text. Eighth International Conference on Weblogs and Social Media (ICWSM-14). Ann Arbor, MI, June 2014.
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"""
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import time
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from pubsublogger import publisher
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from Helper import Process
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from nltk.sentiment.vader import SentimentIntensityAnalyzer
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from nltk import tokenize
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def Analyse(message):
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path = message
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paste = Paste.paste(path)
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content = paste.p_get_content()
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sentences = tokenize.sent_tokenize(content.decode('utf-8', 'ignore'))
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sid = SentimentIntensityAnalyzer()
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for sentence in sentences:
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print(sentence)
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ss = sid.polarity_scores(sentence)
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for k in sorted(ss):
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print('{0}: {1}, '.format(k, ss[k]))
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print ''
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if __name__ == '__main__':
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# If you wish to use an other port of channel, do not forget to run a subscriber accordingly (see launch_logs.sh)
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# Port of the redis instance used by pubsublogger
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publisher.port = 6380
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# Script is the default channel used for the modules.
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publisher.channel = 'Script'
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# Section name in bin/packages/modules.cfg
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config_section = '<section name>'
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# Setup the I/O queues
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p = Process(config_section)
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# Sent to the logging a description of the module
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publisher.info("<description of the module>")
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# Endless loop getting messages from the input queue
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while True:
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# Get one message from the input queue
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message = p.get_from_set()
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if message is None:
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publisher.debug("{} queue is empty, waiting".format(config_section))
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time.sleep(1)
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continue
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# Do something with the message from the queue
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Analyse(message)
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