634 lines
21 KiB
Python
634 lines
21 KiB
Python
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# Natural Language Toolkit: vader
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#
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# Copyright (C) 2001-2023 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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# Malavika Suresh <malavika.suresh0794@gmail.com> (modifications)
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# URL: <https://www.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 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 nltk.util import pairwise
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class VaderConstants:
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"""
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A class to keep the Vader lists and constants.
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"""
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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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NEGATE = {
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"aint",
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"arent",
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"cannot",
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"cant",
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"couldnt",
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"darent",
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"didnt",
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"doesnt",
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"ain't",
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"aren't",
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"can't",
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"couldn't",
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"daren't",
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"didn't",
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"doesn't",
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"dont",
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"hadnt",
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"hasnt",
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"havent",
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"isnt",
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"mightnt",
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"mustnt",
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"neither",
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"don't",
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"hadn't",
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"hasn't",
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"haven't",
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"isn't",
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"mightn't",
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"mustn't",
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"neednt",
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"needn't",
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"never",
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"none",
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"nope",
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"nor",
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"not",
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"nothing",
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"nowhere",
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"oughtnt",
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"shant",
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"shouldnt",
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"uhuh",
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"wasnt",
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"werent",
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"oughtn't",
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"shan't",
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"shouldn't",
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"uh-uh",
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"wasn't",
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"weren't",
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"without",
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"wont",
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"wouldnt",
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"won't",
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"wouldn't",
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"rarely",
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"seldom",
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"despite",
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}
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# booster/dampener 'intensifiers' or 'degree adverbs'
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# https://en.wiktionary.org/wiki/Category:English_degree_adverbs
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BOOSTER_DICT = {
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"absolutely": B_INCR,
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"amazingly": B_INCR,
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"awfully": B_INCR,
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"completely": B_INCR,
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"considerably": B_INCR,
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"decidedly": B_INCR,
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"deeply": B_INCR,
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"effing": B_INCR,
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"enormously": B_INCR,
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"entirely": B_INCR,
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"especially": B_INCR,
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"exceptionally": B_INCR,
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"extremely": B_INCR,
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"fabulously": B_INCR,
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"flipping": B_INCR,
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"flippin": B_INCR,
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"fricking": B_INCR,
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"frickin": B_INCR,
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"frigging": B_INCR,
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"friggin": B_INCR,
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"fully": B_INCR,
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"fucking": B_INCR,
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"greatly": B_INCR,
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"hella": B_INCR,
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"highly": B_INCR,
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"hugely": B_INCR,
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"incredibly": B_INCR,
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"intensely": B_INCR,
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"majorly": B_INCR,
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"more": B_INCR,
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"most": B_INCR,
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"particularly": B_INCR,
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"purely": B_INCR,
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"quite": B_INCR,
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"really": B_INCR,
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"remarkably": B_INCR,
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"so": B_INCR,
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"substantially": B_INCR,
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"thoroughly": B_INCR,
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"totally": B_INCR,
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"tremendously": B_INCR,
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"uber": B_INCR,
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"unbelievably": B_INCR,
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"unusually": B_INCR,
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"utterly": B_INCR,
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"very": B_INCR,
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"almost": B_DECR,
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"barely": B_DECR,
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"hardly": B_DECR,
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"just enough": B_DECR,
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"kind of": B_DECR,
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"kinda": B_DECR,
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"kindof": B_DECR,
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"kind-of": B_DECR,
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"less": B_DECR,
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"little": B_DECR,
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"marginally": B_DECR,
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"occasionally": B_DECR,
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"partly": B_DECR,
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"scarcely": B_DECR,
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"slightly": B_DECR,
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"somewhat": B_DECR,
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"sort of": B_DECR,
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"sorta": B_DECR,
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"sortof": B_DECR,
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"sort-of": B_DECR,
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}
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# check for special case idioms using a sentiment-laden keyword known to SAGE
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SPECIAL_CASE_IDIOMS = {
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"the shit": 3,
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"the bomb": 3,
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"bad ass": 1.5,
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"yeah right": -2,
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"cut the mustard": 2,
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"kiss of death": -1.5,
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"hand to mouth": -2,
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}
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# for removing punctuation
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REGEX_REMOVE_PUNCTUATION = re.compile(f"[{re.escape(string.punctuation)}]")
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PUNC_LIST = [
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".",
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"!",
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"?",
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",",
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";",
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":",
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"-",
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"'",
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'"',
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"!!",
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"!!!",
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"??",
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"???",
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"?!?",
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"!?!",
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"?!?!",
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"!?!?",
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]
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def __init__(self):
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pass
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def negated(self, 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 = self.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(self, 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 scalar_inc_dec(self, 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 self.BOOSTER_DICT:
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scalar = self.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 += self.C_INCR
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else:
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scalar -= self.C_INCR
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return scalar
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class SentiText:
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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, punc_list, regex_remove_punctuation):
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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.PUNC_LIST = punc_list
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self.REGEX_REMOVE_PUNCTUATION = regex_remove_punctuation
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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 = self.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 = self.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 = {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(self.PUNC_LIST, words_only)}
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punc_after = {"".join(p): p[0] for p in product(words_only, self.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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def allcap_differential(self, 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 0 < cap_differential < len(words):
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is_different = True
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return is_different
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class SentimentIntensityAnalyzer:
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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__(
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self,
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lexicon_file="sentiment/vader_lexicon.zip/vader_lexicon/vader_lexicon.txt",
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):
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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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self.constants = VaderConstants()
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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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:note: Hashtags are not taken into consideration (e.g. #BAD is neutral). If you
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are interested in processing the text in the hashtags too, then we recommend
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preprocessing your data to remove the #, after which the hashtag text may be
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matched as if it was a normal word in the sentence.
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"""
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# text, words_and_emoticons, is_cap_diff = self.preprocess(text)
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sentitext = SentiText(
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text, self.constants.PUNC_LIST, self.constants.REGEX_REMOVE_PUNCTUATION
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)
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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 (
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i < len(words_and_emoticons) - 1
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and item.lower() == "kind"
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and words_and_emoticons[i + 1].lower() == "of"
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) or item.lower() in self.constants.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)
|
||
|
|
|
||
|
|
sentiments = self._but_check(words_and_emoticons, sentiments)
|
||
|
|
|
||
|
|
return self.score_valence(sentiments, text)
|
||
|
|
|
||
|
|
def sentiment_valence(self, valence, sentitext, item, i, sentiments):
|
||
|
|
is_cap_diff = sentitext.is_cap_diff
|
||
|
|
words_and_emoticons = sentitext.words_and_emoticons
|
||
|
|
item_lowercase = item.lower()
|
||
|
|
if item_lowercase in self.lexicon:
|
||
|
|
# get the sentiment valence
|
||
|
|
valence = self.lexicon[item_lowercase]
|
||
|
|
|
||
|
|
# check if sentiment laden word is in ALL CAPS (while others aren't)
|
||
|
|
if item.isupper() and is_cap_diff:
|
||
|
|
if valence > 0:
|
||
|
|
valence += self.constants.C_INCR
|
||
|
|
else:
|
||
|
|
valence -= self.constants.C_INCR
|
||
|
|
|
||
|
|
for start_i in range(0, 3):
|
||
|
|
if (
|
||
|
|
i > start_i
|
||
|
|
and words_and_emoticons[i - (start_i + 1)].lower()
|
||
|
|
not in self.lexicon
|
||
|
|
):
|
||
|
|
# dampen the scalar modifier of preceding words and emoticons
|
||
|
|
# (excluding the ones that immediately preceed the item) based
|
||
|
|
# on their distance from the current item.
|
||
|
|
s = self.constants.scalar_inc_dec(
|
||
|
|
words_and_emoticons[i - (start_i + 1)], valence, is_cap_diff
|
||
|
|
)
|
||
|
|
if start_i == 1 and s != 0:
|
||
|
|
s = s * 0.95
|
||
|
|
if start_i == 2 and s != 0:
|
||
|
|
s = s * 0.9
|
||
|
|
valence = valence + s
|
||
|
|
valence = self._never_check(
|
||
|
|
valence, words_and_emoticons, start_i, i
|
||
|
|
)
|
||
|
|
if start_i == 2:
|
||
|
|
valence = self._idioms_check(valence, words_and_emoticons, i)
|
||
|
|
|
||
|
|
# future work: consider other sentiment-laden idioms
|
||
|
|
# other_idioms =
|
||
|
|
# {"back handed": -2, "blow smoke": -2, "blowing smoke": -2,
|
||
|
|
# "upper hand": 1, "break a leg": 2,
|
||
|
|
# "cooking with gas": 2, "in the black": 2, "in the red": -2,
|
||
|
|
# "on the ball": 2,"under the weather": -2}
|
||
|
|
|
||
|
|
valence = self._least_check(valence, words_and_emoticons, i)
|
||
|
|
|
||
|
|
sentiments.append(valence)
|
||
|
|
return sentiments
|
||
|
|
|
||
|
|
def _least_check(self, valence, words_and_emoticons, i):
|
||
|
|
# check for negation case using "least"
|
||
|
|
if (
|
||
|
|
i > 1
|
||
|
|
and words_and_emoticons[i - 1].lower() not in self.lexicon
|
||
|
|
and words_and_emoticons[i - 1].lower() == "least"
|
||
|
|
):
|
||
|
|
if (
|
||
|
|
words_and_emoticons[i - 2].lower() != "at"
|
||
|
|
and words_and_emoticons[i - 2].lower() != "very"
|
||
|
|
):
|
||
|
|
valence = valence * self.constants.N_SCALAR
|
||
|
|
elif (
|
||
|
|
i > 0
|
||
|
|
and words_and_emoticons[i - 1].lower() not in self.lexicon
|
||
|
|
and words_and_emoticons[i - 1].lower() == "least"
|
||
|
|
):
|
||
|
|
valence = valence * self.constants.N_SCALAR
|
||
|
|
return valence
|
||
|
|
|
||
|
|
def _but_check(self, words_and_emoticons, sentiments):
|
||
|
|
words_and_emoticons = [w_e.lower() for w_e in words_and_emoticons]
|
||
|
|
but = {"but"} & set(words_and_emoticons)
|
||
|
|
if but:
|
||
|
|
bi = words_and_emoticons.index(next(iter(but)))
|
||
|
|
for sidx, sentiment in enumerate(sentiments):
|
||
|
|
if sidx < bi:
|
||
|
|
sentiments[sidx] = sentiment * 0.5
|
||
|
|
elif sidx > bi:
|
||
|
|
sentiments[sidx] = sentiment * 1.5
|
||
|
|
return sentiments
|
||
|
|
|
||
|
|
def _idioms_check(self, valence, words_and_emoticons, i):
|
||
|
|
onezero = f"{words_and_emoticons[i - 1]} {words_and_emoticons[i]}"
|
||
|
|
|
||
|
|
twoonezero = "{} {} {}".format(
|
||
|
|
words_and_emoticons[i - 2],
|
||
|
|
words_and_emoticons[i - 1],
|
||
|
|
words_and_emoticons[i],
|
||
|
|
)
|
||
|
|
|
||
|
|
twoone = f"{words_and_emoticons[i - 2]} {words_and_emoticons[i - 1]}"
|
||
|
|
|
||
|
|
threetwoone = "{} {} {}".format(
|
||
|
|
words_and_emoticons[i - 3],
|
||
|
|
words_and_emoticons[i - 2],
|
||
|
|
words_and_emoticons[i - 1],
|
||
|
|
)
|
||
|
|
|
||
|
|
threetwo = "{} {}".format(
|
||
|
|
words_and_emoticons[i - 3], words_and_emoticons[i - 2]
|
||
|
|
)
|
||
|
|
|
||
|
|
sequences = [onezero, twoonezero, twoone, threetwoone, threetwo]
|
||
|
|
|
||
|
|
for seq in sequences:
|
||
|
|
if seq in self.constants.SPECIAL_CASE_IDIOMS:
|
||
|
|
valence = self.constants.SPECIAL_CASE_IDIOMS[seq]
|
||
|
|
break
|
||
|
|
|
||
|
|
if len(words_and_emoticons) - 1 > i:
|
||
|
|
zeroone = f"{words_and_emoticons[i]} {words_and_emoticons[i + 1]}"
|
||
|
|
if zeroone in self.constants.SPECIAL_CASE_IDIOMS:
|
||
|
|
valence = self.constants.SPECIAL_CASE_IDIOMS[zeroone]
|
||
|
|
if len(words_and_emoticons) - 1 > i + 1:
|
||
|
|
zeroonetwo = "{} {} {}".format(
|
||
|
|
words_and_emoticons[i],
|
||
|
|
words_and_emoticons[i + 1],
|
||
|
|
words_and_emoticons[i + 2],
|
||
|
|
)
|
||
|
|
if zeroonetwo in self.constants.SPECIAL_CASE_IDIOMS:
|
||
|
|
valence = self.constants.SPECIAL_CASE_IDIOMS[zeroonetwo]
|
||
|
|
|
||
|
|
# check for booster/dampener bi-grams such as 'sort of' or 'kind of'
|
||
|
|
if (
|
||
|
|
threetwo in self.constants.BOOSTER_DICT
|
||
|
|
or twoone in self.constants.BOOSTER_DICT
|
||
|
|
):
|
||
|
|
valence = valence + self.constants.B_DECR
|
||
|
|
return valence
|
||
|
|
|
||
|
|
def _never_check(self, valence, words_and_emoticons, start_i, i):
|
||
|
|
if start_i == 0:
|
||
|
|
if self.constants.negated([words_and_emoticons[i - 1]]):
|
||
|
|
valence = valence * self.constants.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 self.constants.negated([words_and_emoticons[i - (start_i + 1)]]):
|
||
|
|
valence = valence * self.constants.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 self.constants.negated([words_and_emoticons[i - (start_i + 1)]]):
|
||
|
|
valence = valence * self.constants.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 = self.constants.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
|