/
transformation.py
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/
transformation.py
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import math
import util
from pattern.en import wordnet
from pattern.en import NOUN, VERB, ADJECTIVE, ADVERB
from textblob import Word
from lexicons import SentiWords
__sentiwords = SentiWords()
ATTENUATORS_ADVERBS = open('groups_of_adverbs/medium_attenuator_adv.txt','r').readlines()
ATTENUATORS_ADVERBS = ATTENUATORS_ADVERBS + open('groups_of_adverbs/strong_attenuator_adv.txt','r').readlines()
ATTENUATORS_ADVERBS = ATTENUATORS_ADVERBS + open('groups_of_adverbs/weak_attenuator_adv.txt','r').readlines()
INTENSIFIERS_ADVERBS = open('groups_of_adverbs/medium_intensifier_adv.txt','r').readlines()
INTENSIFIERS_ADVERBS = INTENSIFIERS_ADVERBS + open('groups_of_adverbs/strong_intensifier_adv.txt','r').readlines()
INTENSIFIERS_ADVERBS = INTENSIFIERS_ADVERBS + open('groups_of_adverbs/weak_intensifier_adv.txt','r').readlines()
NON_GRADING_ADVERBS = open('groups_of_adverbs/non_grading_adv.txt','r').readlines()
def word_polarity(word, pos_tag=None, prior_polarity_score=False, linear_score=None):
"""returns a (polarity, subjectivity)-tuple for the given word from SENTIWORDNET.
If there is no synsets for the given word, None will be returned
The word can be NOUN, VERB, ADJECTIVE, ADVERB"""
if prior_polarity_score:
return __word_prior_polarity(word, pos_tag, linear_score)
pos_tag = "NOUN" if pos_tag in util.PENN_NOUNS_TAGS else pos_tag
pos_tag = "VERB" if pos_tag in util.PENN_VERBS_TAGS else pos_tag
pos_tag = "ADVERB" if pos_tag in util.PENN_ADVERBS_TAGS else pos_tag
pos_tag = "ADJECTIVE" if pos_tag in util.PENN_ADJECTIVES_TAGS else None
TAGS = {"NOUN":NOUN, "VERB":VERB, "ADJECTIVE":ADJECTIVE, "ADVERB":ADVERB}
TAG = TAGS[pos_tag] if pos_tag else ADJECTIVE
synsets = wordnet.synsets(word['raw'], TAG)
if len(synsets) > 0:
polarity = synsets[0].weight
if linear_score:
polarity[0] = polarity[0] * (word['index'] / linear_score['doc_size']) * linear_score['linear_score_constant']
return polarity
else:
return None
def __word_prior_polarity(word, pos_tag=None, linear_score=None):
pos_tag = "n" if pos_tag in util.PENN_NOUNS_TAGS else pos_tag
pos_tag = "v" if pos_tag in util.PENN_VERBS_TAGS else pos_tag
pos_tag = "r" if pos_tag in util.PENN_ADVERBS_TAGS else pos_tag
pos_tag = "a" if pos_tag in util.PENN_ADJECTIVES_TAGS else None
if pos_tag is None:
pos_tag = 'a'
prior_polarity_score = __sentiwords.get_entry_by_name_and_pos(word['raw'],pos_tag)
if prior_polarity_score is None:
return None
if linear_score:
prior_polarity_score['prior_polarity_score'] = prior_polarity_score['prior_polarity_score'] * (float(word['index']) / float(linear_score['doc_size'])) * linear_score['linear_score_constant']
return (prior_polarity_score['prior_polarity_score'], 0)
def is_negation(bigram_first_word):
"""Gets the fist word of a bigram and checks if this words is a negation or contraction word"""
NEGATION_WORDS = ['no','not']
NEGATION_CONTRACTIONS = ["isn't","aren't","wasn't","weren't","haven't",
"hasn't","hadn't","won't","wouldn't","don't",
"doesn't","didn't","can't","couldn't","shouldn't"
"mightn't","mustn't","ain't","mayn't","oughtn't",
"shan't"]
return (bigram_first_word in NEGATION_WORDS) or (bigram_first_word in NEGATION_CONTRACTIONS)
def invert_polarity(polarity, type=None):
"""It inverts or do a complement of the polarity"""
if type == 'complement':
if polarity < 0:
return -(1.0 - abs(polarity))
else:
return 1.0 - polarity
return -1.0 * polarity
def apply_adverb_factor(adverb, polarity, negation=None):
if is_negation(adverb):
return invert_polarity(polarity, negation)
bigram_polarity = 0.0
type = 1 #non-grading again
polarity = float(polarity)
factor = 1.0 #assumes that is non_grading by default
for att_adv in ATTENUATORS_ADVERBS:
if adverb in att_adv:
values = att_adv.split('\n')[0]
values = values.split(';')
factor = float(values[1])
type = 2
break
if factor == 1.0: #did not find nothing in attenuators
for int_adv in INTENSIFIERS_ADVERBS:
if adverb in int_adv:
values = int_adv.split('\n')[0]
values = values.split(';')
factor = float(values[1])
type = 3
break
if type == 3:
if polarity < 0:
#print 'adverb + polarity: ' + str(- math.pow(abs(polarity), 1.0 / factor))
return (- math.pow(abs(polarity), 1.0 / factor))
else:
#print 'adverb + polarity: ' + str(math.pow(polarity, 1.0 / factor))
return (math.pow(polarity, 1.0 / factor))
elif type == 2:
if polarity < 0:
#print 'adverb + polarity: ' + str(- math.pow(abs(polarity), factor))
return (- math.pow(abs(polarity), factor))
else:
#print 'adverb + polarity: ' + str(math.pow(polarity,factor))
return (math.pow(polarity,factor))
elif type == 1:
return polarity
def default_adv_xxx_bigram_polarity(bigram, negation=None, prior_polarity_score=False, linear_score=None):
"""Calculates the bigram polarity based on a empirical factor from each adverb group
and SENTIWORDNET word polarity
"""
second_word_polarity = word_polarity(bigram['second_word'],
bigram['second_word']['tag'],
prior_polarity_score = prior_polarity_score,
linear_score = linear_score)
# If is a verb, tries again in lemmatized form
if bigram['second_word']['tag'] in util.PENN_VERBS_TAGS and \
(second_word_polarity == None or second_word_polarity[0] == 0):
w = Word(bigram['second_word']['raw'])
bigram['second_word']['lemma'] = w.lemmatize("v")
second_word_polarity = word_polarity(bigram['second_word'],
bigram['second_word']['tag'],
prior_polarity_score = prior_polarity_score,
linear_score = linear_score)
#if the ngram_2 does not have polarity, so stops the method
if second_word_polarity == None:
return None
return apply_adverb_factor(bigram['first_word']['raw'],second_word_polarity[0], negation)
def adjectives_polarities(list_of_adjectives, prior_polarity_score=False, linear_score=None):
"""This method calculates all adjectives polarities based on the following arguments
Keyword arguments:
list_of_adjectives -- list of adjectives from a document
"""
adjectives_polarities = []
for adjective in list_of_adjectives:
polarity = word_polarity(adjective,
prior_polarity_score = prior_polarity_score,
linear_score = linear_score)
if polarity and polarity[0] != 0.0:
adjectives_polarities.append(polarity[0])
return adjectives_polarities
def adv_adj_bigrams_polarities(list_of_adv_adj_bigrams, negation=None, prior_polarity_score=False, linear_score=None):
"""This method calculates all bigrams polarities based on the following arguments
Keyword arguments:
list_of_adv_adj_bigrams -- list of bigrams in the following format: ADVERB / ADJECTIVE
"""
bigrams_polarities = []
for bigram in list_of_adv_adj_bigrams:
bigram_polarity = default_adv_xxx_bigram_polarity(bigram,
negation,
prior_polarity_score=prior_polarity_score,
linear_score=linear_score)
if bigram_polarity:
bigrams_polarities.append(bigram_polarity)
return bigrams_polarities
def trigram_polarity(trigram, negation=None, prior_polarity_score=False):
first_word = trigram[0]
middle_word = trigram[1]
third_word = trigram[2]
#words
first_word_word = first_word.split('/')[0]
#word tags
middle_word_tag = middle_word.split('/')[1]
third_word_tag = third_word.split('/')[1]
results = []
#adv/adv/adj trigram
if middle_word_tag in util.PENN_ADVERBS_TAGS and third_word_tag in util.PENN_ADJECTIVES_TAGS:
parcial_result = default_adv_xxx_bigram_polarity((middle_word,third_word), negation, prior_polarity_score=prior_polarity_score)
if parcial_result == None:
return None
parcial_result = apply_adverb_factor(first_word_word,parcial_result)
if parcial_result != None and abs(parcial_result) != 0:
results.append(parcial_result)
return results
return results
def ngrams_polarities(ngrams_list, negation=None, prior_polarity_score=False, linear_score=None):
"""
Given a list of ngrams (such as "good, bad, (very,good),awesome"), returns a list of corresponding polarities
"""
polarities = []
for ngram in ngrams_list:
pol = 0
if len(ngram) == 2: #bigrams - adverbs and adjectives
pol = default_adv_xxx_bigram_polarity(ngram,
negation,
prior_polarity_score = prior_polarity_score,
linear_score = linear_score)
else: #unigrams - adjectives
pol = word_polarity(ngram,
prior_polarity_score=prior_polarity_score,
linear_score = linear_score)
if pol != None and type(pol) is tuple and pol[0] != 0:
polarities.append(pol[0])
elif pol != None and (type(pol) is int or type(pol) is float) and pol != 0:
polarities.append(pol)
return polarities
def ngrams_matrix_polarities(ngrams_matrix, negation=None, prior_polarity_score=False, linear_score=None):
"""Given a matrix of ngrams (or a list of ngrams list), return a matrix of its corresponding polarities"""
polarities_matrix = {}
for _id, ngrams_list in ngrams_matrix.iteritems():
polarities_matrix[_id] = ngrams_polarities(ngrams_list,
negation,
prior_polarity_score=prior_polarity_score,
linear_score=linear_score)
return polarities_matrix