sentiment.py

#!/usr/bin/python 
#
# (originally entered at https://gist.github.com/1035399)
#
# License: GPLv3
#
# To download the AFINN word list do:
# wget http://www2.imm.dtu.dk/pubdb/views/edoc_download.php/6010/zip/imm6010.zip
# unzip imm6010.zip
#
# Note that for pedagogic reasons there is a UNICODE/UTF-8 error in the code.
 
import math
import re
import sys
reload(sys)
sys.setdefaultencoding('utf-8')
 
# AFINN-111 is as of June 2011 the most recent version of AFINN
filenameAFINN = 'AFINN/AFINN-111.txt'
afinn = dict(map(lambda (w, s): (w, int(s)), [ 
            ws.strip().split('\t') for ws in open(filenameAFINN) ]))
 
# Word splitter pattern
pattern_split = re.compile(r"\W+")
 
def sentiment(text):
    """
    Returns a float for sentiment strength based on the input text.
    Positive values are positive valence, negative value are negative valence. 
    """
    words = pattern_split.split(text.lower())
    sentiments = map(lambda word: afinn.get(word, 0), words)
    if sentiments:
        # How should you weight the individual word sentiments? 
        # You could do N, sqrt(N) or 1 for example. Here I use sqrt(N)
        sentiment = float(sum(sentiments))/math.sqrt(len(sentiments))
        
    else:
        sentiment = 0
    return sentiment
 
 
 
if __name__ == '__main__':
    # Single sentence example:
    text = sys.argv[1]
    #"Finn is stupid and idiotic"
    print("%6.2f %s" % (sentiment(text), text))
    
    # No negation and booster words handled in this approach
    #text = "Finn is only a tiny bit stupid and not idiotic"
    #print("%6.2f %s" % (sentiment(text), text))
 
    ''' 
    # Example with downloading from Twitter:
    import simplejson
    import urllib
 
    query = "pfizer"
    json = simplejson.load(urllib.urlopen("http://search.twitter.com/search.json?q=" + query))
    sentiments = map(sentiment, [ tweet['text'] for tweet in json['results'] ])
    print("%6.2f %s" % (sum(sentiments)/math.sqrt(len(sentiments)), query))
    '''