mirror of
https://github.com/gsi-upm/senpy
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369 lines
20 KiB
Python
369 lines
20 KiB
Python
#!/usr/bin/python
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# coding: utf-8
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'''
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Created on July 04, 2013
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@author: C.J. Hutto
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Citation Information
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If you use any of the VADER sentiment analysis tools
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(VADER sentiment lexicon or Python code for rule-based sentiment
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analysis engine) in your work or research, please cite the paper.
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For example:
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Hutto, C.J. & Gilbert, E.E. (2014). VADER: A Parsimonious Rule-based Model for
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Sentiment Analysis of Social Media Text. Eighth International Conference on
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Weblogs and Social Media (ICWSM-14). Ann Arbor, MI, June 2014.
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'''
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import os, math, re, sys, fnmatch, string
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import codecs
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def make_lex_dict(f):
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maps = {}
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with codecs.open(f, encoding='iso-8859-1') as f:
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for wmsr in f:
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w, m = wmsr.strip().split('\t')[:2]
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maps[w] = m
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return maps
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f = 'vader_sentiment_lexicon.txt' # empirically derived valence ratings for words, emoticons, slang, swear words, acronyms/initialisms
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try:
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word_valence_dict = make_lex_dict(f)
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except:
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f = os.path.join(os.path.dirname(__file__),'vader_sentiment_lexicon.txt')
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word_valence_dict = make_lex_dict(f)
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# for removing punctuation
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regex_remove_punctuation = re.compile('[%s]' % re.escape(string.punctuation))
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def sentiment(text):
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"""
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Returns a float for sentiment strength based on the input text.
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Positive values are positive valence, negative value are negative valence.
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"""
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wordsAndEmoticons = str(text).split() #doesn't separate words from adjacent punctuation (keeps emoticons & contractions)
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text_mod = regex_remove_punctuation.sub('', text) # removes punctuation (but loses emoticons & contractions)
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wordsOnly = str(text_mod).split()
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# get rid of empty items or single letter "words" like 'a' and 'I' from wordsOnly
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for word in wordsOnly:
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if len(word) <= 1:
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wordsOnly.remove(word)
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# now remove adjacent & redundant punctuation from [wordsAndEmoticons] while keeping emoticons and contractions
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puncList = [".", "!", "?", ",", ";", ":", "-", "'", "\"",
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"!!", "!!!", "??", "???", "?!?", "!?!", "?!?!", "!?!?"]
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for word in wordsOnly:
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for p in puncList:
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pword = p + word
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x1 = wordsAndEmoticons.count(pword)
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while x1 > 0:
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i = wordsAndEmoticons.index(pword)
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wordsAndEmoticons.remove(pword)
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wordsAndEmoticons.insert(i, word)
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x1 = wordsAndEmoticons.count(pword)
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wordp = word + p
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x2 = wordsAndEmoticons.count(wordp)
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while x2 > 0:
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i = wordsAndEmoticons.index(wordp)
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wordsAndEmoticons.remove(wordp)
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wordsAndEmoticons.insert(i, word)
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x2 = wordsAndEmoticons.count(wordp)
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# get rid of residual empty items or single letter "words" like 'a' and 'I' from wordsAndEmoticons
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for word in wordsAndEmoticons:
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if len(word) <= 1:
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wordsAndEmoticons.remove(word)
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# remove stopwords from [wordsAndEmoticons]
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#stopwords = [str(word).strip() for word in open('stopwords.txt')]
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#for word in wordsAndEmoticons:
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# if word in stopwords:
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# wordsAndEmoticons.remove(word)
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# check for negation
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negate = ["aint", "arent", "cannot", "cant", "couldnt", "darent", "didnt", "doesnt",
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"ain't", "aren't", "can't", "couldn't", "daren't", "didn't", "doesn't",
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"dont", "hadnt", "hasnt", "havent", "isnt", "mightnt", "mustnt", "neither",
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"don't", "hadn't", "hasn't", "haven't", "isn't", "mightn't", "mustn't",
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"neednt", "needn't", "never", "none", "nope", "nor", "not", "nothing", "nowhere",
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"oughtnt", "shant", "shouldnt", "uhuh", "wasnt", "werent",
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"oughtn't", "shan't", "shouldn't", "uh-uh", "wasn't", "weren't",
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"without", "wont", "wouldnt", "won't", "wouldn't", "rarely", "seldom", "despite"]
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def negated(list, nWords=[], includeNT=True):
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nWords.extend(negate)
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for word in nWords:
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if word in list:
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return True
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if includeNT:
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for word in list:
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if "n't" in word:
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return True
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if "least" in list:
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i = list.index("least")
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if i > 0 and list[i-1] != "at":
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return True
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return False
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def normalize(score, alpha=15):
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# normalize the score to be between -1 and 1 using an alpha that approximates the max expected value
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normScore = score/math.sqrt( ((score*score) + alpha) )
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return normScore
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def wildCardMatch(patternWithWildcard, listOfStringsToMatchAgainst):
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listOfMatches = fnmatch.filter(listOfStringsToMatchAgainst, patternWithWildcard)
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return listOfMatches
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def isALLCAP_differential(wordList):
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countALLCAPS= 0
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for w in wordList:
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if str(w).isupper():
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countALLCAPS += 1
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cap_differential = len(wordList) - countALLCAPS
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if cap_differential > 0 and cap_differential < len(wordList):
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isDiff = True
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else: isDiff = False
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return isDiff
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isCap_diff = isALLCAP_differential(wordsAndEmoticons)
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b_incr = 0.293 #(empirically derived mean sentiment intensity rating increase for booster words)
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b_decr = -0.293
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# booster/dampener 'intensifiers' or 'degree adverbs' http://en.wiktionary.org/wiki/Category:English_degree_adverbs
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booster_dict = {"absolutely": b_incr, "amazingly": b_incr, "awfully": b_incr, "completely": b_incr, "considerably": b_incr,
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"decidedly": b_incr, "deeply": b_incr, "effing": b_incr, "enormously": b_incr,
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"entirely": b_incr, "especially": b_incr, "exceptionally": b_incr, "extremely": b_incr,
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"fabulously": b_incr, "flipping": b_incr, "flippin": b_incr,
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"fricking": b_incr, "frickin": b_incr, "frigging": b_incr, "friggin": b_incr, "fully": b_incr, "fucking": b_incr,
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"greatly": b_incr, "hella": b_incr, "highly": b_incr, "hugely": b_incr, "incredibly": b_incr,
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"intensely": b_incr, "majorly": b_incr, "more": b_incr, "most": b_incr, "particularly": b_incr,
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"purely": b_incr, "quite": b_incr, "really": b_incr, "remarkably": b_incr,
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"so": b_incr, "substantially": b_incr,
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"thoroughly": b_incr, "totally": b_incr, "tremendously": b_incr,
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"uber": b_incr, "unbelievably": b_incr, "unusually": b_incr, "utterly": b_incr,
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"very": b_incr,
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"almost": b_decr, "barely": b_decr, "hardly": b_decr, "just enough": b_decr,
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"kind of": b_decr, "kinda": b_decr, "kindof": b_decr, "kind-of": b_decr,
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"less": b_decr, "little": b_decr, "marginally": b_decr, "occasionally": b_decr, "partly": b_decr,
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"scarcely": b_decr, "slightly": b_decr, "somewhat": b_decr,
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"sort of": b_decr, "sorta": b_decr, "sortof": b_decr, "sort-of": b_decr}
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sentiments = []
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for item in wordsAndEmoticons:
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v = 0
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i = wordsAndEmoticons.index(item)
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if (i < len(wordsAndEmoticons)-1 and str(item).lower() == "kind" and \
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str(wordsAndEmoticons[i+1]).lower() == "of") or str(item).lower() in booster_dict:
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sentiments.append(v)
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continue
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item_lowercase = str(item).lower()
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if item_lowercase in word_valence_dict:
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#get the sentiment valence
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v = float(word_valence_dict[item_lowercase])
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#check if sentiment laden word is in ALLCAPS (while others aren't)
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c_incr = 0.733 #(empirically derived mean sentiment intensity rating increase for using ALLCAPs to emphasize a word)
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if str(item).isupper() and isCap_diff:
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if v > 0: v += c_incr
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else: v -= c_incr
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#check if the preceding words increase, decrease, or negate/nullify the valence
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def scalar_inc_dec(word, valence):
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scalar = 0.0
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word_lower = str(word).lower()
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if word_lower in booster_dict:
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scalar = booster_dict[word_lower]
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if valence < 0: scalar *= -1
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#check if booster/dampener word is in ALLCAPS (while others aren't)
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if str(word).isupper() and isCap_diff:
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if valence > 0: scalar += c_incr
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else: scalar -= c_incr
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return scalar
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n_scalar = -0.74
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if i > 0 and str(wordsAndEmoticons[i-1]).lower() not in word_valence_dict:
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s1 = scalar_inc_dec(wordsAndEmoticons[i-1], v)
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v = v+s1
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if negated([wordsAndEmoticons[i-1]]): v = v*n_scalar
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if i > 1 and str(wordsAndEmoticons[i-2]).lower() not in word_valence_dict:
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s2 = scalar_inc_dec(wordsAndEmoticons[i-2], v)
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if s2 != 0: s2 = s2*0.95
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v = v+s2
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# check for special use of 'never' as valence modifier instead of negation
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if wordsAndEmoticons[i-2] == "never" and (wordsAndEmoticons[i-1] == "so" or wordsAndEmoticons[i-1] == "this"):
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v = v*1.5
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# otherwise, check for negation/nullification
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elif negated([wordsAndEmoticons[i-2]]): v = v*n_scalar
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if i > 2 and str(wordsAndEmoticons[i-3]).lower() not in word_valence_dict:
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s3 = scalar_inc_dec(wordsAndEmoticons[i-3], v)
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if s3 != 0: s3 = s3*0.9
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v = v+s3
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# check for special use of 'never' as valence modifier instead of negation
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if wordsAndEmoticons[i-3] == "never" and \
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(wordsAndEmoticons[i-2] == "so" or wordsAndEmoticons[i-2] == "this") or \
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(wordsAndEmoticons[i-1] == "so" or wordsAndEmoticons[i-1] == "this"):
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v = v*1.25
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# otherwise, check for negation/nullification
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elif negated([wordsAndEmoticons[i-3]]): v = v*n_scalar
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# check for special case idioms using a sentiment-laden keyword known to SAGE
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special_case_idioms = {"the shit": 3, "the bomb": 3, "bad ass": 1.5, "yeah right": -2,
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"cut the mustard": 2, "kiss of death": -1.5, "hand to mouth": -2}
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# future work: consider other sentiment-laden idioms
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#other_idioms = {"back handed": -2, "blow smoke": -2, "blowing smoke": -2, "upper hand": 1, "break a leg": 2,
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# "cooking with gas": 2, "in the black": 2, "in the red": -2, "on the ball": 2,"under the weather": -2}
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onezero = "{} {}".format(str(wordsAndEmoticons[i-1]), str(wordsAndEmoticons[i]))
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twoonezero = "{} {} {}".format(str(wordsAndEmoticons[i-2]), str(wordsAndEmoticons[i-1]), str(wordsAndEmoticons[i]))
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twoone = "{} {}".format(str(wordsAndEmoticons[i-2]), str(wordsAndEmoticons[i-1]))
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threetwoone = "{} {} {}".format(str(wordsAndEmoticons[i-3]), str(wordsAndEmoticons[i-2]), str(wordsAndEmoticons[i-1]))
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threetwo = "{} {}".format(str(wordsAndEmoticons[i-3]), str(wordsAndEmoticons[i-2]))
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if onezero in special_case_idioms: v = special_case_idioms[onezero]
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elif twoonezero in special_case_idioms: v = special_case_idioms[twoonezero]
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elif twoone in special_case_idioms: v = special_case_idioms[twoone]
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elif threetwoone in special_case_idioms: v = special_case_idioms[threetwoone]
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elif threetwo in special_case_idioms: v = special_case_idioms[threetwo]
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if len(wordsAndEmoticons)-1 > i:
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zeroone = "{} {}".format(str(wordsAndEmoticons[i]), str(wordsAndEmoticons[i+1]))
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if zeroone in special_case_idioms: v = special_case_idioms[zeroone]
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if len(wordsAndEmoticons)-1 > i+1:
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zeroonetwo = "{} {}".format(str(wordsAndEmoticons[i]), str(wordsAndEmoticons[i+1]), str(wordsAndEmoticons[i+2]))
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if zeroonetwo in special_case_idioms: v = special_case_idioms[zeroonetwo]
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# check for booster/dampener bi-grams such as 'sort of' or 'kind of'
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if threetwo in booster_dict or twoone in booster_dict:
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v = v+b_decr
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# check for negation case using "least"
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if i > 1 and str(wordsAndEmoticons[i-1]).lower() not in word_valence_dict \
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and str(wordsAndEmoticons[i-1]).lower() == "least":
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if (str(wordsAndEmoticons[i-2]).lower() != "at" and str(wordsAndEmoticons[i-2]).lower() != "very"):
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v = v*n_scalar
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elif i > 0 and str(wordsAndEmoticons[i-1]).lower() not in word_valence_dict \
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and str(wordsAndEmoticons[i-1]).lower() == "least":
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v = v*n_scalar
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sentiments.append(v)
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# check for modification in sentiment due to contrastive conjunction 'but'
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if 'but' in wordsAndEmoticons or 'BUT' in wordsAndEmoticons:
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try: bi = wordsAndEmoticons.index('but')
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except: bi = wordsAndEmoticons.index('BUT')
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for s in sentiments:
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si = sentiments.index(s)
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if si < bi:
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sentiments.pop(si)
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sentiments.insert(si, s*0.5)
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elif si > bi:
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sentiments.pop(si)
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sentiments.insert(si, s*1.5)
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if sentiments:
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sum_s = float(sum(sentiments))
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#print sentiments, sum_s
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# check for added emphasis resulting from exclamation points (up to 4 of them)
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ep_count = str(text).count("!")
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if ep_count > 4: ep_count = 4
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ep_amplifier = ep_count*0.292 #(empirically derived mean sentiment intensity rating increase for exclamation points)
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if sum_s > 0: sum_s += ep_amplifier
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elif sum_s < 0: sum_s -= ep_amplifier
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# check for added emphasis resulting from question marks (2 or 3+)
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qm_count = str(text).count("?")
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qm_amplifier = 0
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if qm_count > 1:
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if qm_count <= 3: qm_amplifier = qm_count*0.18
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else: qm_amplifier = 0.96
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if sum_s > 0: sum_s += qm_amplifier
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elif sum_s < 0: sum_s -= qm_amplifier
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compound = normalize(sum_s)
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# want separate positive versus negative sentiment scores
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pos_sum = 0.0
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neg_sum = 0.0
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neu_count = 0
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for sentiment_score in sentiments:
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if sentiment_score > 0:
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pos_sum += (float(sentiment_score) +1) # compensates for neutral words that are counted as 1
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if sentiment_score < 0:
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neg_sum += (float(sentiment_score) -1) # when used with math.fabs(), compensates for neutrals
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if sentiment_score == 0:
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neu_count += 1
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if pos_sum > math.fabs(neg_sum): pos_sum += (ep_amplifier+qm_amplifier)
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elif pos_sum < math.fabs(neg_sum): neg_sum -= (ep_amplifier+qm_amplifier)
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total = pos_sum + math.fabs(neg_sum) + neu_count
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pos = math.fabs(pos_sum / total)
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neg = math.fabs(neg_sum / total)
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neu = math.fabs(neu_count / total)
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else:
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compound = 0.0; pos = 0.0; neg = 0.0; neu = 0.0
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s = {"neg" : round(neg, 3),
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"neu" : round(neu, 3),
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"pos" : round(pos, 3),
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"compound" : round(compound, 4)}
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return s
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if __name__ == '__main__':
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# --- examples -------
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sentences = [
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"VADER is smart, handsome, and funny.", # positive sentence example
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"VADER is smart, handsome, and funny!", # punctuation emphasis handled correctly (sentiment intensity adjusted)
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"VADER is very smart, handsome, and funny.", # booster words handled correctly (sentiment intensity adjusted)
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"VADER is VERY SMART, handsome, and FUNNY.", # emphasis for ALLCAPS handled
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"VADER is VERY SMART, handsome, and FUNNY!!!",# combination of signals - VADER appropriately adjusts intensity
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"VADER is VERY SMART, really handsome, and INCREDIBLY FUNNY!!!",# booster words & punctuation make this close to ceiling for score
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"The book was good.", # positive sentence
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"The book was kind of good.", # qualified positive sentence is handled correctly (intensity adjusted)
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"The plot was good, but the characters are uncompelling and the dialog is not great.", # mixed negation sentence
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"A really bad, horrible book.", # negative sentence with booster words
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"At least it isn't a horrible book.", # negated negative sentence with contraction
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":) and :D", # emoticons handled
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"", # an empty string is correctly handled
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"Today sux", # negative slang handled
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"Today sux!", # negative slang with punctuation emphasis handled
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"Today SUX!", # negative slang with capitalization emphasis
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"Today kinda sux! But I'll get by, lol" # mixed sentiment example with slang and constrastive conjunction "but"
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]
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paragraph = "It was one of the worst movies I've seen, despite good reviews. \
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Unbelievably bad acting!! Poor direction. VERY poor production. \
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The movie was bad. Very bad movie. VERY bad movie. VERY BAD movie. VERY BAD movie!"
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from nltk import tokenize
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lines_list = tokenize.sent_tokenize(paragraph)
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sentences.extend(lines_list)
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tricky_sentences = [
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"Most automated sentiment analysis tools are shit.",
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"VADER sentiment analysis is the shit.",
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"Sentiment analysis has never been good.",
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"Sentiment analysis with VADER has never been this good.",
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"Warren Beatty has never been so entertaining.",
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"I won't say that the movie is astounding and I wouldn't claim that the movie is too banal either.",
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"I like to hate Michael Bay films, but I couldn't fault this one",
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"It's one thing to watch an Uwe Boll film, but another thing entirely to pay for it",
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"The movie was too good",
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"This movie was actually neither that funny, nor super witty.",
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"This movie doesn't care about cleverness, wit or any other kind of intelligent humor.",
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"Those who find ugly meanings in beautiful things are corrupt without being charming.",
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"There are slow and repetitive parts, BUT it has just enough spice to keep it interesting.",
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"The script is not fantastic, but the acting is decent and the cinematography is EXCELLENT!",
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"Roger Dodger is one of the most compelling variations on this theme.",
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"Roger Dodger is one of the least compelling variations on this theme.",
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"Roger Dodger is at least compelling as a variation on the theme.",
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"they fall in love with the product",
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"but then it breaks",
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"usually around the time the 90 day warranty expires",
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"the twin towers collapsed today",
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"However, Mr. Carter solemnly argues, his client carried out the kidnapping under orders and in the ''least offensive way possible.''"
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]
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sentences.extend(tricky_sentences)
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for sentence in sentences:
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print(sentence)
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ss = sentiment(sentence)
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print("\t" + str(ss))
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print("\n\n Done!")
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