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tomOpinionMining3.py
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tomOpinionMining3.py
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from nltk import word_tokenize,pos_tag
from nltk.stem import WordNetLemmatizer
from nltk.wsd import lesk
import numpy as np
import re
from nltk.corpus import sentiwordnet as swn
from nltk.corpus import wordnet as wn
# tokenize and pos tag
def toke_n_tag(text):
pos_tagged_text = pos_tag(word_tokenize(text))
return (pos_tagged_text,text)
# perform analysis current
def sentiment_analysis(text_n_tagged_text):
pos_tagged_text = text_n_tagged_text[0]
text = text_n_tagged_text[1]
pos_arr = []
neg_arr = []
subj_arr = []
for obj in pos_tagged_text:
if return_pos_sentiwordnet(obj[1]) == 0:
continue
pos = return_pos_sentiwordnet(obj[1])
polarity = polarity_score_2(obj[0],pos)
subj = subjectivity_score_2(obj[0],pos)
subj_arr.append(subj)
if polarity > 0.0:
pos_arr.append(polarity)
elif polarity < 0.0:
neg_arr.append(polarity)
else:
continue
#print pos_arr
if np.array(pos_arr).size == 0:
pos_mean_score = 0.0
else:
pos_mean_score = round(np.mean(np.array(pos_arr)),1)
if np.array(neg_arr).size == 0:
neg_mean_score = 0.0
else:
neg_mean_score = round(np.mean(np.array(neg_arr)),1)
subj_mean_score = round(np.mean(np.array(subj_arr)),1)
temp_neg_score = neg_mean_score * -1.0
#if (pos_mean_score,neg_mean_score,subj_mean_score):
#return (pos_mean_score,neg_mean_score,subj_mean_score)
#else:
#return (0,0.0)
if pos_mean_score > temp_neg_score:
return ('1',pos_mean_score + neg_mean_score,subj_mean_score)
elif pos_mean_score < temp_neg_score:
return ('-1',pos_mean_score + neg_mean_score,subj_mean_score)
else:
return ('0',0.0,subj_mean_score)
# perform analysis with WSD
def sentiment_analysis_wsd(text_n_tagged_text):
pos_tagged_text = text_n_tagged_text[0]
text = text_n_tagged_text[1]
pos_arr = []
neg_arr = []
subj_arr = []
for obj in pos_tagged_text:
if return_pos_sentiwordnet(obj[1]) == 0:
continue
pos = return_pos_sentiwordnet(obj[1])
if lesk(text,obj[0],pos):
syn = lesk(text,obj[0],pos)
polarity = polarity_score_1(syn)
subj = subjectivity_score_1(syn)
else:
polarity = polarity_score_2(obj[0],pos)
subj = subjectivity_score_2(obj[0],pos)
subj_arr.append(subj)
if polarity > 0.0:
pos_arr.append(polarity)
elif polarity < 0.0:
neg_arr.append(polarity)
else:
continue
#print pos_arr
if np.array(pos_arr).size == 0:
pos_mean_score = 0.0
else:
pos_mean_score = round(np.mean(np.array(pos_arr)),1)
if np.array(neg_arr).size == 0:
neg_mean_score = 0.0
else:
neg_mean_score = round(np.mean(np.array(neg_arr)),1)
subj_mean_score = round(np.mean(np.array(subj_arr)),1)
temp_neg_score = neg_mean_score * -1.0
#if (pos_mean_score,neg_mean_score,subj_mean_score):
#return (pos_mean_score,neg_mean_score,subj_mean_score)
#else:
#return (0,0.0)
if pos_mean_score > temp_neg_score:
return ('1',pos_mean_score + neg_mean_score,subj_mean_score)
elif pos_mean_score < temp_neg_score:
return ('-1',pos_mean_score + neg_mean_score,subj_mean_score)
else:
return ('0',0.0,subj_mean_score)
# perform analysis old
def sentiment_analysis_old(text_n_tagged_text):
lemma = WordNetLemmatizer()
pos_tagged_text = text_n_tagged_text[0]
text = text_n_tagged_text[1]
n_text = new_sent_after_lemma(text)
pos_arr = []
neg_arr = []
subj_arr = []
polarity_word_arr =[]
subj_word_arr =[]
for obj in pos_tagged_text:
#lem = lemma.lemmatize(obj[0])
if return_pos_sentiwordnet(obj[1]) == 0:
continue
pos = return_pos_sentiwordnet(obj[1])
if lesk(text,obj[0],pos):
#if lesk(n_text,lem,pos):
#syn = lesk(text,obj[0],pos)
#syn = lesk(n_text,lem,pos)
#polarity = polarity_score_1(syn)
polarity = polarity_score_2(obj[0],pos)
#polarity = polarity_score_2(lem,pos)
#subj = subjectivity_score_1(syn)
subj = subjectivity_score_2(obj[0],pos)
#subj = subjectivity_score_2(lem,pos)
elif re.match(r'[a-zA-Z]*-[a-zA-Z]*',obj[0]):
polarity = polarity_score_2(obj[0],pos)
subj = subjectivity_score_2(obj[0],pos)
else:
polarity = polarity_score_2(obj[0],pos)
subj = subjectivity_score_2(obj[0],pos)
#continue
subj_arr.append(subj)
polarity_word_arr.append((obj[0],polarity))
subj_word_arr.append((obj[0],subj))
if polarity > 0.0:
pos_arr.append(polarity)
elif polarity < 0.0:
neg_arr.append(polarity)
else:
continue
#print subj_word_arr
pos_mean_score = round(np.mean(np.array(pos_arr)),2)
neg_mean_score = round(np.mean(np.array(neg_arr)),2)
subj_mean_score = round(np.mean(np.array(subj_arr)),2)
scores = {'pos':pos_mean_score,'neg':neg_mean_score,'subj':subj_mean_score}
temp_neg_score = neg_mean_score * -1.0
if pos_mean_score > temp_neg_score:
return ('1',pos_mean_score + neg_mean_score,subj_mean_score)
elif pos_mean_score < temp_neg_score:
return ('-1',pos_mean_score + neg_mean_score,subj_mean_score)
else:
return ('0',0.0,subj_mean_score)
def subjectivity_score_1(s):
return swn.senti_synset(s.name()).obj_score()
def subjectivity_score_2(word,pos):
neut_arr = []
if len(swn.senti_synsets(word,pos)) == 0:
return 0.0
for s in swn.senti_synsets(word,pos):
neut_arr.append(s.obj_score())
#print neut_arr
return round(np.mean(np.array(neut_arr)),2)
def polarity_score_1(s):
pos = swn.senti_synset(s.name()).pos_score()
neg = swn.senti_synset(s.name()).neg_score()
subj = swn.senti_synset(s.name()).obj_score()
#print "%s,%s,%s,%s" %(s.name(),pos,neg,subj)
if pos > neg:
return pos
elif neg > pos:
return neg * -1.0
elif pos == 0.0 and neg == 0.0:
return 0.0
else:
return 0.0
def polarity_score_2(word,pos):
pos_arr = []
neg_arr = []
neut_arr = []
if len(swn.senti_synsets(word,pos)) == 0:
return 0.0
for s in swn.senti_synsets(word,pos):
pos_arr.append(s.pos_score())
neg_arr.append(s.neg_score())
neut_arr.append(s.obj_score())
pos = round(np.mean(np.array(pos_arr)),2)
neg = round(np.mean(np.array(neg_arr)),2)
subj = round(np.mean(np.array(neut_arr)),2)
#return "%s,%s,%s,%s" %(word,pos,neg,subj)
#print "%s,%s,%s,%s" %(word,pos,neg,subj)
if pos > neg :
return pos
elif neg > pos:
#return float('-' + str(neg))
return neg * -1.0
elif pos == 0.0 and neg == 0.0:
return 0.0
else:
return 0.0
def return_pos_sentiwordnet(pos):
if pos[:2] == 'NN':
return 'n'
elif pos[:2] == 'VB':
return 'v'
elif pos[:2] == 'JJ':
return 'a'
elif pos[:2] == 'RB':
return 'r'
else:
return 0
def new_sent_after_lemma(text):
lemma = WordNetLemmatizer()
new_text = []
for word in pos_tag(word_tokenize(text)):
pos = return_pos_sentiwordnet(word[1])
if pos:
new_w = lemma.lemmatize(word[0],pos = pos )
new_text.append(new_w)
else:
new_w = word[0]
new_text.append(new_w)
return ' '.join(new_text)