444 lines
18 KiB
Python
444 lines
18 KiB
Python
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# coding: utf-8
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# Natural Language Toolkit: vader
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#
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# Copyright (C) 2001-2018 NLTK Project
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# Author: C.J. Hutto <Clayton.Hutto@gtri.gatech.edu>
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# Ewan Klein <ewan@inf.ed.ac.uk> (modifications)
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# Pierpaolo Pantone <24alsecondo@gmail.com> (modifications)
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# George Berry <geb97@cornell.edu> (modifications)
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# URL: <http://nltk.org/>
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# For license information, see LICENSE.TXT
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#
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# Modifications to the original VADER code have been made in order to
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# integrate it into NLTK. These have involved changes to
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# ensure Python 3 compatibility, and refactoring to achieve greater modularity.
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"""
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If you use the VADER sentiment analysis tools, please cite:
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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 codecs
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import math
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import re
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import string
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from itertools import product
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import nltk.data
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from .util import pairwise
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##Constants##
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# (empirically derived mean sentiment intensity rating increase for booster words)
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B_INCR = 0.293
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B_DECR = -0.293
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# (empirically derived mean sentiment intensity rating increase for using
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# ALLCAPs to emphasize a word)
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C_INCR = 0.733
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N_SCALAR = -0.74
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# for removing punctuation
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REGEX_REMOVE_PUNCTUATION = re.compile('[{0}]'.format(re.escape(string.punctuation)))
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PUNC_LIST = [".", "!", "?", ",", ";", ":", "-", "'", "\"",
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"!!", "!!!", "??", "???", "?!?", "!?!", "?!?!", "!?!?"]
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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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# booster/dampener 'intensifiers' or 'degree adverbs'
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# http://en.wiktionary.org/wiki/Category:English_degree_adverbs
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BOOSTER_DICT = \
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{"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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# 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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##Static methods##
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def negated(input_words, include_nt=True):
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"""
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Determine if input contains negation words
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"""
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neg_words = NEGATE
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if any(word.lower() in neg_words for word in input_words):
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return True
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if include_nt:
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if any("n't" in word.lower() for word in input_words):
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return True
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for first, second in pairwise(input_words):
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if second.lower() == "least" and first.lower() != '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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"""
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Normalize the score to be between -1 and 1 using an alpha that
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approximates the max expected value
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"""
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norm_score = score/math.sqrt((score*score) + alpha)
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return norm_score
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def allcap_differential(words):
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"""
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Check whether just some words in the input are ALL CAPS
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:param list words: The words to inspect
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:returns: `True` if some but not all items in `words` are ALL CAPS
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"""
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is_different = False
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allcap_words = 0
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for word in words:
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if word.isupper():
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allcap_words += 1
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cap_differential = len(words) - allcap_words
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if cap_differential > 0 and cap_differential < len(words):
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is_different = True
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return is_different
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def scalar_inc_dec(word, valence, is_cap_diff):
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"""
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Check if the preceding words increase, decrease, or negate/nullify the
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valence
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"""
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scalar = 0.0
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word_lower = 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:
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scalar *= -1
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#check if booster/dampener word is in ALLCAPS (while others aren't)
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if word.isupper() and is_cap_diff:
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if valence > 0:
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scalar += C_INCR
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else: scalar -= C_INCR
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return scalar
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class SentiText(object):
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"""
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Identify sentiment-relevant string-level properties of input text.
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"""
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def __init__(self, text):
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if not isinstance(text, str):
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text = str(text.encode('utf-8'))
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self.text = text
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self.words_and_emoticons = self._words_and_emoticons()
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# doesn't separate words from\
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# adjacent punctuation (keeps emoticons & contractions)
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self.is_cap_diff = allcap_differential(self.words_and_emoticons)
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def _words_plus_punc(self):
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"""
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Returns mapping of form:
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{
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'cat,': 'cat',
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',cat': 'cat',
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}
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"""
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no_punc_text = REGEX_REMOVE_PUNCTUATION.sub('', self.text)
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# removes punctuation (but loses emoticons & contractions)
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words_only = no_punc_text.split()
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# remove singletons
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words_only = set( w for w in words_only if len(w) > 1 )
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# the product gives ('cat', ',') and (',', 'cat')
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punc_before = {''.join(p): p[1] for p in product(PUNC_LIST, words_only)}
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punc_after = {''.join(p): p[0] for p in product(words_only, PUNC_LIST)}
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words_punc_dict = punc_before
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words_punc_dict.update(punc_after)
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return words_punc_dict
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def _words_and_emoticons(self):
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"""
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Removes leading and trailing puncutation
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Leaves contractions and most emoticons
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Does not preserve punc-plus-letter emoticons (e.g. :D)
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"""
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wes = self.text.split()
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words_punc_dict = self._words_plus_punc()
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wes = [we for we in wes if len(we) > 1]
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for i, we in enumerate(wes):
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if we in words_punc_dict:
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wes[i] = words_punc_dict[we]
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return wes
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class SentimentIntensityAnalyzer(object):
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"""
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Give a sentiment intensity score to sentences.
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"""
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def __init__(self, lexicon_file="sentiment/vader_lexicon.zip/vader_lexicon/vader_lexicon.txt"):
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self.lexicon_file = nltk.data.load(lexicon_file)
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self.lexicon = self.make_lex_dict()
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def make_lex_dict(self):
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"""
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Convert lexicon file to a dictionary
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"""
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lex_dict = {}
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for line in self.lexicon_file.split('\n'):
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(word, measure) = line.strip().split('\t')[0:2]
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lex_dict[word] = float(measure)
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return lex_dict
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def polarity_scores(self, text):
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"""
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Return a float for sentiment strength based on the input text.
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Positive values are positive valence, negative value are negative
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valence.
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"""
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sentitext = SentiText(text)
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#text, words_and_emoticons, is_cap_diff = self.preprocess(text)
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sentiments = []
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words_and_emoticons = sentitext.words_and_emoticons
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for item in words_and_emoticons:
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valence = 0
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i = words_and_emoticons.index(item)
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if (i < len(words_and_emoticons) - 1 and item.lower() == "kind" and \
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words_and_emoticons[i+1].lower() == "of") or \
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item.lower() in BOOSTER_DICT:
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sentiments.append(valence)
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continue
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sentiments = self.sentiment_valence(valence, sentitext, item, i, sentiments)
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sentiments = self._but_check(words_and_emoticons, sentiments)
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return self.score_valence(sentiments, text)
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def sentiment_valence(self, valence, sentitext, item, i, sentiments):
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is_cap_diff = sentitext.is_cap_diff
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words_and_emoticons = sentitext.words_and_emoticons
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item_lowercase = item.lower()
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if item_lowercase in self.lexicon:
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#get the sentiment valence
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valence = self.lexicon[item_lowercase]
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#check if sentiment laden word is in ALL CAPS (while others aren't)
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if item.isupper() and is_cap_diff:
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if valence > 0:
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valence += C_INCR
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else:
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valence -= C_INCR
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for start_i in range(0,3):
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if i > start_i and words_and_emoticons[i-(start_i+1)].lower() not in self.lexicon:
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# dampen the scalar modifier of preceding words and emoticons
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# (excluding the ones that immediately preceed the item) based
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# on their distance from the current item.
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s = scalar_inc_dec(words_and_emoticons[i-(start_i+1)], valence, is_cap_diff)
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if start_i == 1 and s != 0:
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s = s*0.95
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if start_i == 2 and s != 0:
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s = s*0.9
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valence = valence+s
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valence = self._never_check(valence, words_and_emoticons, start_i, i)
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if start_i == 2:
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valence = self._idioms_check(valence, words_and_emoticons, i)
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# future work: consider other sentiment-laden idioms
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# other_idioms =
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# {"back handed": -2, "blow smoke": -2, "blowing smoke": -2,
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# "upper hand": 1, "break a leg": 2,
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# "cooking with gas": 2, "in the black": 2, "in the red": -2,
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# "on the ball": 2,"under the weather": -2}
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valence = self._least_check(valence, words_and_emoticons, i)
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sentiments.append(valence)
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return sentiments
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def _least_check(self, valence, words_and_emoticons, i):
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# check for negation case using "least"
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if i > 1 and words_and_emoticons[i-1].lower() not in self.lexicon \
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and words_and_emoticons[i-1].lower() == "least":
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if words_and_emoticons[i-2].lower() != "at" and words_and_emoticons[i-2].lower() != "very":
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valence = valence*N_SCALAR
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elif i > 0 and words_and_emoticons[i-1].lower() not in self.lexicon \
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and words_and_emoticons[i-1].lower() == "least":
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valence = valence*N_SCALAR
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return valence
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def _but_check(self, words_and_emoticons, sentiments):
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# check for modification in sentiment due to contrastive conjunction 'but'
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if 'but' in words_and_emoticons or 'BUT' in words_and_emoticons:
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try:
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bi = words_and_emoticons.index('but')
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except ValueError:
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bi = words_and_emoticons.index('BUT')
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for sentiment in sentiments:
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si = sentiments.index(sentiment)
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if si < bi:
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sentiments.pop(si)
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sentiments.insert(si, sentiment*0.5)
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elif si > bi:
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sentiments.pop(si)
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sentiments.insert(si, sentiment*1.5)
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return sentiments
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def _idioms_check(self, valence, words_and_emoticons, i):
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onezero = "{0} {1}".format(words_and_emoticons[i-1], words_and_emoticons[i])
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twoonezero = "{0} {1} {2}".format(words_and_emoticons[i-2],
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words_and_emoticons[i-1], words_and_emoticons[i])
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twoone = "{0} {1}".format(words_and_emoticons[i-2], words_and_emoticons[i-1])
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threetwoone = "{0} {1} {2}".format(words_and_emoticons[i-3],
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words_and_emoticons[i-2], words_and_emoticons[i-1])
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threetwo = "{0} {1}".format(words_and_emoticons[i-3], words_and_emoticons[i-2])
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sequences = [onezero, twoonezero, twoone, threetwoone, threetwo]
|
||
|
|
|
||
|
|
for seq in sequences:
|
||
|
|
if seq in SPECIAL_CASE_IDIOMS:
|
||
|
|
valence = SPECIAL_CASE_IDIOMS[seq]
|
||
|
|
break
|
||
|
|
|
||
|
|
if len(words_and_emoticons)-1 > i:
|
||
|
|
zeroone = "{0} {1}".format(words_and_emoticons[i], words_and_emoticons[i+1])
|
||
|
|
if zeroone in SPECIAL_CASE_IDIOMS:
|
||
|
|
valence = SPECIAL_CASE_IDIOMS[zeroone]
|
||
|
|
if len(words_and_emoticons)-1 > i+1:
|
||
|
|
zeroonetwo = "{0} {1} {2}".format(words_and_emoticons[i], words_and_emoticons[i+1], words_and_emoticons[i+2])
|
||
|
|
if zeroonetwo in SPECIAL_CASE_IDIOMS:
|
||
|
|
valence = SPECIAL_CASE_IDIOMS[zeroonetwo]
|
||
|
|
|
||
|
|
# check for booster/dampener bi-grams such as 'sort of' or 'kind of'
|
||
|
|
if threetwo in BOOSTER_DICT or twoone in BOOSTER_DICT:
|
||
|
|
valence = valence+B_DECR
|
||
|
|
return valence
|
||
|
|
|
||
|
|
def _never_check(self, valence, words_and_emoticons, start_i, i):
|
||
|
|
if start_i == 0:
|
||
|
|
if negated([words_and_emoticons[i-1]]):
|
||
|
|
valence = valence*N_SCALAR
|
||
|
|
if start_i == 1:
|
||
|
|
if words_and_emoticons[i-2] == "never" and\
|
||
|
|
(words_and_emoticons[i-1] == "so" or
|
||
|
|
words_and_emoticons[i-1] == "this"):
|
||
|
|
valence = valence*1.5
|
||
|
|
elif negated([words_and_emoticons[i-(start_i+1)]]):
|
||
|
|
valence = valence*N_SCALAR
|
||
|
|
if start_i == 2:
|
||
|
|
if words_and_emoticons[i-3] == "never" and \
|
||
|
|
(words_and_emoticons[i-2] == "so" or words_and_emoticons[i-2] == "this") or \
|
||
|
|
(words_and_emoticons[i-1] == "so" or words_and_emoticons[i-1] == "this"):
|
||
|
|
valence = valence*1.25
|
||
|
|
elif negated([words_and_emoticons[i-(start_i+1)]]):
|
||
|
|
valence = valence*N_SCALAR
|
||
|
|
return valence
|
||
|
|
|
||
|
|
def _punctuation_emphasis(self, sum_s, text):
|
||
|
|
# add emphasis from exclamation points and question marks
|
||
|
|
ep_amplifier = self._amplify_ep(text)
|
||
|
|
qm_amplifier = self._amplify_qm(text)
|
||
|
|
punct_emph_amplifier = ep_amplifier+qm_amplifier
|
||
|
|
return punct_emph_amplifier
|
||
|
|
|
||
|
|
def _amplify_ep(self, text):
|
||
|
|
# check for added emphasis resulting from exclamation points (up to 4 of them)
|
||
|
|
ep_count = text.count("!")
|
||
|
|
if ep_count > 4:
|
||
|
|
ep_count = 4
|
||
|
|
# (empirically derived mean sentiment intensity rating increase for
|
||
|
|
# exclamation points)
|
||
|
|
ep_amplifier = ep_count*0.292
|
||
|
|
return ep_amplifier
|
||
|
|
|
||
|
|
def _amplify_qm(self, text):
|
||
|
|
# check for added emphasis resulting from question marks (2 or 3+)
|
||
|
|
qm_count = text.count("?")
|
||
|
|
qm_amplifier = 0
|
||
|
|
if qm_count > 1:
|
||
|
|
if qm_count <= 3:
|
||
|
|
# (empirically derived mean sentiment intensity rating increase for
|
||
|
|
# question marks)
|
||
|
|
qm_amplifier = qm_count*0.18
|
||
|
|
else:
|
||
|
|
qm_amplifier = 0.96
|
||
|
|
return qm_amplifier
|
||
|
|
|
||
|
|
def _sift_sentiment_scores(self, sentiments):
|
||
|
|
# want separate positive versus negative sentiment scores
|
||
|
|
pos_sum = 0.0
|
||
|
|
neg_sum = 0.0
|
||
|
|
neu_count = 0
|
||
|
|
for sentiment_score in sentiments:
|
||
|
|
if sentiment_score > 0:
|
||
|
|
pos_sum += (float(sentiment_score) +1) # compensates for neutral words that are counted as 1
|
||
|
|
if sentiment_score < 0:
|
||
|
|
neg_sum += (float(sentiment_score) -1) # when used with math.fabs(), compensates for neutrals
|
||
|
|
if sentiment_score == 0:
|
||
|
|
neu_count += 1
|
||
|
|
return pos_sum, neg_sum, neu_count
|
||
|
|
|
||
|
|
def score_valence(self, sentiments, text):
|
||
|
|
if sentiments:
|
||
|
|
sum_s = float(sum(sentiments))
|
||
|
|
# compute and add emphasis from punctuation in text
|
||
|
|
punct_emph_amplifier = self._punctuation_emphasis(sum_s, text)
|
||
|
|
if sum_s > 0:
|
||
|
|
sum_s += punct_emph_amplifier
|
||
|
|
elif sum_s < 0:
|
||
|
|
sum_s -= punct_emph_amplifier
|
||
|
|
|
||
|
|
compound = normalize(sum_s)
|
||
|
|
# discriminate between positive, negative and neutral sentiment scores
|
||
|
|
pos_sum, neg_sum, neu_count = self._sift_sentiment_scores(sentiments)
|
||
|
|
|
||
|
|
if pos_sum > math.fabs(neg_sum):
|
||
|
|
pos_sum += (punct_emph_amplifier)
|
||
|
|
elif pos_sum < math.fabs(neg_sum):
|
||
|
|
neg_sum -= (punct_emph_amplifier)
|
||
|
|
|
||
|
|
total = pos_sum + math.fabs(neg_sum) + neu_count
|
||
|
|
pos = math.fabs(pos_sum / total)
|
||
|
|
neg = math.fabs(neg_sum / total)
|
||
|
|
neu = math.fabs(neu_count / total)
|
||
|
|
|
||
|
|
else:
|
||
|
|
compound = 0.0
|
||
|
|
pos = 0.0
|
||
|
|
neg = 0.0
|
||
|
|
neu = 0.0
|
||
|
|
|
||
|
|
sentiment_dict = \
|
||
|
|
{"neg" : round(neg, 3),
|
||
|
|
"neu" : round(neu, 3),
|
||
|
|
"pos" : round(pos, 3),
|
||
|
|
"compound" : round(compound, 4)}
|
||
|
|
|
||
|
|
return sentiment_dict
|