-
Notifications
You must be signed in to change notification settings - Fork 4
/
Copy pathsentiwordnet.py
185 lines (162 loc) · 6.03 KB
/
sentiwordnet.py
1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16
17
18
19
20
21
22
23
24
25
26
27
28
29
30
31
32
33
34
35
36
37
38
39
40
41
42
43
44
45
46
47
48
49
50
51
52
53
54
55
56
57
58
59
60
61
62
63
64
65
66
67
68
69
70
71
72
73
74
75
76
77
78
79
80
81
82
83
84
85
86
87
88
89
90
91
92
93
94
95
96
97
98
99
100
101
102
103
104
105
106
107
108
109
110
111
112
113
114
115
116
117
118
119
120
121
122
123
124
125
126
127
128
129
130
131
132
133
134
135
136
137
138
139
140
141
142
143
144
145
146
147
148
149
150
151
152
153
154
155
156
157
158
159
160
161
162
163
164
165
166
167
168
169
170
171
172
173
174
175
176
177
178
179
180
181
182
183
184
185
from __future__ import division
import nltk
# 1. CC Coordinating conjunction
# 2. CD Cardinal number
# 3. DT Determiner
# 4. EX Existential there
# 5. FW Foreign word
# 6. IN Preposition or subordinating conjunction
# 7. JJ Adjective
# 8. JJR Adjective, comparative
# 9. JJS Adjective, superlative
# 10. LS List item marker
# 11. MD Modal
# 12. NN Noun, singular or mass
# 13. NNS Noun, plural
# 14. NNP Proper noun, singular
# 15. NNPS Proper noun, plural
# 16. PDT Predeterminer
# 17. POS Possessive ending
# 18. PRP Personal pronoun
# 19. PRP$ Possessive pronoun
# 20. RB Adverb
# 21. RBR Adverb, comparative
# 22. RBS Adverb, superlative
# 23. RP Particle
# 24. SYM Symbol
# 25. TO to
# 26. UH Interjection
# 27. VB Verb, base form
# 28. VBD Verb, past tense
# 29. VBG Verb, gerund or present participle
# 30. VBN Verb, past participle
# 31. VBP Verb, non-3rd person singular present
# 32. VBZ Verb, 3rd person singular present
# 33. WDT Wh-determiner
# 34. WP Wh-pronoun
# 35. WP$ Possessive wh-pronoun
# 36. WRB Wh-adverb
class SentiWordNet():
def __init__(self, netpath):
self.netpath = netpath
self.dictionary = {}
def infoextract(self):
tempdict = {}
f = open(self.netpath, "r")
# Example line:
# POS ID PosS NegS SynsetTerm#sensenumber Desc
# a 00009618 0.5 0.25 spartan#4 austere#3 ascetical#2 ……
for sor in f.readlines():
if sor.strip().startswith("#"):
pass
else:
data = sor.split("\t")
if len(data) != 6:
print('invalid data')
break
word_type_marker = data[0]
synset_score = float(data[2]) - float(data[3]) # // Calculate synset score as score = PosS - NegS
syn_terms_split = data[4].split(" ") # word#sentimentscore
for w in syn_terms_split:
syn_term_and_rank = w.split("#")
syn_term = syn_term_and_rank[0] + "#" + word_type_marker # 单词#词性
syn_term_rank = int(syn_term_and_rank[1])
if syn_term in tempdict:
t = [syn_term_rank, synset_score]
tempdict.get(syn_term).append(t)
else:
temp = {syn_term: []}
t = [syn_term_rank, synset_score]
temp.get(syn_term).append(t)
tempdict.update(temp)
for key in tempdict.keys():
score = 0.0
ssum = 0.0
for wordlist in tempdict.get(key):
score += wordlist[1] / wordlist[0]
ssum += 1.0 / wordlist[0]
score /= ssum
self.dictionary.update({key: score})
def getscore(self, word, pos):
return self.dictionary.get(word + "#" + pos)
def make_np_vector(np_dict, pos_info):
# 名词（n）、形容词（a）、动词（v）和副词（r）
# 返回向量(正向情感平均值，负向情感平均值，名词占比，形容词占比，动词占比，副词占比)
count_total = len(pos_info)
n_total = 0
a_total = 0
v_total = 0
r_total = 0
pos_total = 0.0
neg_total = 0.0
pos_count = 0
neg_count = 0
noun_set = {'NN', 'NNS', 'NNP', 'NNPS'}
adj_set = {'JJ', 'JJR', 'JJS'}
verb_set = {'VB', 'VBD', 'VBG', 'VBN', 'VBP', 'VBZ'}
adv_set = {'RB', 'RBR', 'RBS', 'WRB'}
vector = []
for i in range(count_total):
pos = 'n'
if pos_info[i][1] in noun_set:
pos = 'n'
n_total += 1
elif pos_info[i][1] in adj_set:
pos = 'a'
a_total += 1
elif pos_info[i][1] in verb_set:
pos = 'v'
v_total += 1
elif pos_info[i][1] in adv_set:
pos = 'r'
r_total += 1
if np_dict.getscore(pos_info[i][0], pos) is not None:
count = np_dict.getscore(pos_info[i][0], pos)
if count > 0:
pos_total += count
pos_count += 1
elif count < 0:
neg_total += count
neg_count += 1
if pos_count > 0:
vector.append(pos_total / pos_count)
else:
vector.append(0)
if neg_count > 0:
vector.append(neg_total / neg_count)
else:
vector.append(0)
if count_total > 0:
vector.append(n_total / count_total)
vector.append(a_total / count_total)
vector.append(v_total / count_total)
vector.append(r_total / count_total)
else:
for _ in range(4):
vector.append(0)
return vector
def test():
net_path = "./data/SentiWordNet.txt"
swn = SentiWordNet(net_path)
swn.infoextract()
sentence = "Don't buy this unless you are willing to pay twice as much for the product you need. Turbo Tax has " \
"lowered themselves to deceptive advertizing as far as I see it. "
text = nltk.word_tokenize(sentence)
pos_info = nltk.pos_tag(text)
return make_np_vector(swn, pos_info)
if __name__ == '__main__':
test()
# print("good#a " + str(swn.getscore('good', 'a')))
# print("good#n " + str(swn.getscore('good', 'n')))
# print("good#r " + str(swn.getscore('good', 'r')))
# print("good#v " + str(swn.getscore('good', 'v')))
#
# print("bad#a " + str(swn.getscore('bad', 'a')))
# print("bad#n " + str(swn.getscore('bad', 'n')))
# print("bad#r " + str(swn.getscore('bad', 'r')))
# print("bad#v " + str(swn.getscore('bad', 'v')))
#
# print("happy#a " + str(swn.getscore('happy', 'a')))
# print("happy#n " + str(swn.getscore('happy', 'n')))
# print("happy#r " + str(swn.getscore('happy', 'r')))
# print("happy#v " + str(swn.getscore('happy', 'v')))