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6
.idea/vcs.xml
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<?xml version="1.0" encoding="UTF-8"?>
<project version="4">
<component name="VcsDirectoryMappings">
<mapping directory="$PROJECT_DIR$" vcs="Git" />
</component>
</project>

2036
SEL.txt
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File diff suppressed because it is too large Load Diff

80
classify_news.py
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from sklearn.feature_extraction.text import TfidfVectorizer
from stopWords import stopWrdList
def getTrnVect():
# code to get the trained vectors
import yaml
str_trained_vect = open('trn_vect.vec', 'r').read().split('\n')
str_trained_vect.pop(len(str_trained_vect)-1)
trained_vect = []
for i in str_trained_vect:
trained_vect.append(yaml.load(i))
del str_trained_vect, i
return trained_vect
def classify_news(document):
# code to vectorize news to classify
from similarityMeasures import cos_sim
vect_to_classify = []
news = open(document, 'r').read()
vect_to_classify.append(news)
stop_words = stopWrdList()
vectorizer = TfidfVectorizer(strip_accents='ascii', analyzer='word', stop_words=stop_words, max_features=100)
X = vectorizer.fit_transform(vect_to_classify)
vector = X.toarray()
trained_vectors = getTrnVect()
# get dim
len_vector = len(vector[0])
len_train = len(trained_vectors[0])
vector = list(vector[0])
if len_train > len_vector:
for i in range(len_train - len_vector):
vector.append(0)
sim_vect = []
for i in trained_vectors:
sim_vect.append(cos_sim(vector, i))
maxi = max(sim_vect)
x = 0
for i in sim_vect:
if i == maxi:
y = x
x = x + 1
part_neu_vect = 'This note has neutral emotions and it is related with the party'
part_neg_vect = 'This note has negative emotions and it is related with the party'
part_pos_vect = 'This note has positive emotions and it is related with the party'
cont_neu_vect = 'This note has neutral emotions and it is related with the opposition'
cont_neg_vect = 'This note has negative emotions and it is related with the opposition'
cont_pos_vect = 'This note has positive emotions and it is related with the opposition'
neut_neu_vect = 'This note has neutral emotions and it is not particularly related to a political party'
neut_neg_vect = 'This note has negative emotions and it is not particularly related to a political party'
neut_pos_vect = 'This note has positive emotions and it is not particularly related to a political party'
results = [part_neu_vect, part_neg_vect, part_pos_vect, cont_neu_vect, cont_neg_vect, cont_pos_vect, neut_neu_vect, neut_neg_vect, neut_pos_vect]
print(results[y])

48
clust.py Executable file → Normal file
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@ -1,49 +1,45 @@
from infBack import get_vect as gv
from sklearn.feature_extraction.text import TfidfVectorizer
from stopWords import stopWrdList
import numpy as np
def clustering():
def stopWrdList():
sw = open('stop.words')
prue = []
prue.append(sw.readlines())
return [l.strip('\n\r') for l in prue[0]]
# This are the relevant news cue words
voc = ["ine", "pri", "pan", "prd", "pt", "pvem", "verde", "movimiento", "ciudadano", "panal", "alianza", "morena", "partido", "encuentro", "social", "electoral"]
stop_words = stopWrdList()
voc = ["ine", "pri", "pan", "prd", "pt", "pvem", "verde", "movimiento", "ciudadano", "panal", "alianza", "morena", "partido", "encuentro", "social", "electoral"]
dataVect = gv()
stop_words = stopWrdList()
dataVect = np.array(dataVect)
dataVect = gv()
corpus = dataVect[:, 2]
dataVect = np.array(dataVect)
vectorizer = TfidfVectorizer(strip_accents='ascii', analyzer='word', stop_words=stop_words, vocabulary=voc)
corpus = dataVect[:, 2]
X = vectorizer.fit_transform(corpus)
vectorizer = TfidfVectorizer(strip_accents='ascii', analyzer='word', stop_words=stop_words, vocabulary=voc)
del dataVect, stop_words, vectorizer # , corpus
X = vectorizer.fit_transform(corpus)
J = X.toarray()
del dataVect, stop_words, vectorizer # , corpus
# The indexes are extracted to obtain only the relevant news from the general corpus
J = X.toarray()
index = []
# print(J)
for x in range(0, len(J)):
index = []
for x in range(0, len(J)):
if sum(J[x]) != 0:
index.append(x)
index = tuple(index)
index = tuple(index)
electCorp = [corpus[x] for x in index]
electCorp = [corpus[x] for x in index]
del corpus
del corpus
# This section of the code processes the political party news in order to give a emotional classification
temp = []
for i in electCorp:
temp.append(i.split(' '))
return temp
print(electCorp)

2
infBack.py
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@ -27,6 +27,8 @@ def get_vect():
return impDat
# print(len(get_vect()))
# this section of the code show how to extract relevant data from the dictionaries
"""

8
main.py
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# from newsTrain import saveTraining
from classify_news import classify_news
# saveTraining()
classify_news('news_to_classify.txt')
classify_news('news2.txt')

382
newsTrain.py
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from sklearn.feature_extraction.text import TfidfVectorizer
from stopWords import stopWrdList
from retEmoDict import emoDic
from clust import clustering
def trainPre(word_array, dict):
default = 'NA'
alegria = []
enojo = []
miedo = []
repulsion = []
sorpresa = []
tristeza = []
proper = []
part = []
for word in word_array:
if dict.get(str(word), default) == 'Alegría':
alegria.append(1)
proper.append(word)
if dict.get(str(word), default) == 'Enojo':
enojo.append(1)
proper.append(word)
if dict.get(str(word), default) == 'Miedo':
miedo.append(1)
proper.append(word)
if dict.get(str(word), default) == 'Repulsión':
repulsion.append(1)
proper.append(word)
if dict.get(str(word), default) == 'Sorpresa':
sorpresa.append(1)
proper.append(word)
if dict.get(str(word), default) == 'Tristeza':
tristeza.append(1)
proper.append(word)
if dict.get(str(word), default) == 'Positivo':
part.append('PRI')
proper.append(word)
if dict.get(str(word), default) == 'Negativo':
part.append('CONTRA')
proper.append(word)
if dict.get(str(word), default) == 'Neutro':
part.append('NEU')
proper.append(word)
if dict.get(str(word), default) == 'NA':
proper.append(word)
part = set(part)
flag = list(part)
vect = set(proper)
vect = list(vect)
tot = len(word_array)
alegria = sum(alegria)
enojo = sum(enojo)
miedo = sum(miedo)
repulsion = sum(repulsion)
sorpresa = sum(sorpresa)
tristeza = sum(tristeza)
pos = (alegria + sorpresa) / tot
neg = (enojo + miedo + repulsion + tristeza) / tot
if len(flag) == 0:
flag = ['NEU']
return [pos, neg, flag, vect]
def corporizer():
emoDict = emoDic()
clust = clustering()
temp = []
for i in clust:
temp.append(trainPre(i, emoDict))
tempy = []
for vect in temp:
tempy.append(' '.join(vect[3]))
return tempy
def flagger():
emoDict = emoDic()
clust = clustering()
temp = []
for i in clust:
temp.append(trainPre(i, emoDict))
flag = []
for j in temp:
#print(j[2])
if j[2] == (['CONTRA', 'NEU', 'PRI'] or ['NEU', 'CONTRA', 'PRI'] or ['NEU', 'PRI', 'CONTRA'] or
['PRI', 'NEU', 'CONTRA'] or ['CONTRA', 'PRI', 'NEU'] or ['PRI', 'CONTRA', 'NEU']):
flag.append(1)
#else:
# flag.append(0)
if j[2] == (['CONTRA', 'PRI'] or ['PRI', 'CONTRA']):
flag.append(1)
#else:
# flag.append(6)
if j[2] == ['NEU']:
flag.append(1)
#else:
# flag.append(7)
if j[2] == (['PRI'] or ['NEU', 'PRI'] or ['PRI', 'NEU']):
flag.append(2)
#else:
# flag.append(8)
if j[2] == (['CONTRA'] or ['NEU', 'CONTRA'] or ['CONTRA', 'NEU']):
flag.append(3)
#else:
# flag.append(9)
index = []
for i in temp:
if i[0] == i[1]:
index.append(1)
if i[0] > i[1]:
index.append(2)
if i[0] < i[1]:
index.append(3)
lenFlag = len(flag)
lenInde = len(index)
if lenFlag < lenInde:
for i in range(lenInde - lenFlag):
flag.append(1)
return (index, flag)
def operate_on_Narray(A, B, function):
try:
return [operate_on_Narray(a, b, function) for a, b in zip(A, B)]
except TypeError as e:
# Not iterable
return function(A, B)
def trainVect():
flag = flagger()
corpus = corporizer()
stop_words = stopWrdList()
vectorizer = TfidfVectorizer(strip_accents='ascii', analyzer='word', stop_words=stop_words, max_features=100)
X = vectorizer.fit_transform(corpus)
vector = X.toarray()
long = len(flag[0])
part_neu_ind = []
part_neg_ind = []
part_pos_ind = []
cont_neu_ind = []
cont_neg_ind = []
cont_pos_ind = []
neut_neu_ind = []
neut_neg_ind = []
neut_pos_ind = []
# flag 0 has emotion info, flag 1 has political party info
# 1 is neutral emo ; 2 is positive emo ; 3 is negative emo
# 1 is neutral ; 2 is pol; 3 is opposition
for s in range(long):
if flag[0][s] == 1 and flag[1][s] == 1:
neut_neu_ind.append(s)
if flag[0][s] == 1 and flag[1][s] == 2:
part_neu_ind.append(s)
if flag[0][s] == 1 and flag[1][s] == 3:
cont_neu_ind.append(s)
if flag[0][s] == 2 and flag[1][s] == 2:
part_pos_ind.append(s)
if flag[0][s] == 2 and flag[1][s] == 3:
cont_pos_ind.append(s)
if flag[0][s] == 2 and flag[1][s] == 1:
neut_pos_ind.append(s)
if flag[0][s] == 3 and flag[1][s] == 1:
neut_neg_ind.append(s)
if flag[0][s] == 3 and flag[1][s] == 2:
part_neg_ind.append(s)
if flag[0][s] == 3 and flag[1][s] == 3:
cont_neg_ind.append(s)
part_neu_vect = [vector[x] for x in part_neu_ind]
part_neg_vect = [vector[x] for x in part_neg_ind]
part_pos_vect = [vector[x] for x in part_pos_ind]
cont_neu_vect = [vector[x] for x in cont_neu_ind]
cont_neg_vect = [vector[x] for x in cont_neg_ind]
cont_pos_vect = [vector[x] for x in cont_pos_ind]
neut_neu_vect = [vector[x] for x in neut_neu_ind]
neut_neg_vect = [vector[x] for x in neut_neg_ind]
neut_pos_vect = [vector[x] for x in neut_pos_ind]
############################################ 1
len1 = len(part_neu_vect)
if len1 != 0:
for a in range(len1):
tmp = part_neu_vect[0]
tmp = operate_on_Narray(part_neu_vect[0], tmp[a+1], lambda x, y: x + y)
tmp = operate_on_Narray(part_neu_vect[0], tmp[a+1], lambda x, y: x / len1)
part_neu_vect = list(tmp)
else:
part_neu_vect = []
############################################ 2
len1 = len(part_neg_vect)
if len1 != 0:
for a in range(len1):
tmp = part_neg_vect[0]
tmp = operate_on_Narray(part_neg_vect[0], tmp[a+1], lambda x, y: x + y)
tmp = operate_on_Narray(part_neg_vect[0], tmp[a+1], lambda x, y: x / len1)
part_neg_vect = list(tmp)
else:
part_neg_vect = []
############################################ 3
len1 = len(part_pos_vect)
if len1 != 0:
for a in range(len1):
tmp = part_pos_vect[0]
tmp = operate_on_Narray(part_pos_vect[0], tmp[a + 1], lambda x, y: x + y)
tmp = operate_on_Narray(part_pos_vect[0], tmp[a + 1], lambda x, y: x / len1)
part_pos_vect = list(tmp)
else:
part_pos_vect = []
############################################ 4
len1 = len(cont_neu_vect)
if len1 != 0:
for a in range(len1):
tmp = cont_neu_vect[0]
tmp = operate_on_Narray(cont_neu_vect[0], tmp[a + 1], lambda x, y: x + y)
tmp = operate_on_Narray(cont_neu_vect[0], tmp[a + 1], lambda x, y: x / len1)
cont_neu_vect = list(tmp)
else:
cont_neu_vect = []
############################################ 5
len1 = len(cont_neg_vect)
if len1 != 0:
for a in range(len1):
tmp = cont_neg_vect[0]
tmp = operate_on_Narray(cont_neg_vect[0], tmp[a + 1], lambda x, y: x + y)
tmp = operate_on_Narray(cont_neg_vect[0], tmp[a + 1], lambda x, y: x / len1)
cont_neg_vect = list(tmp)
else:
cont_neg_vect = []
############################################ 6
len1 = len(cont_pos_vect)
if len1 != 0:
for a in range(len1):
tmp = cont_pos_vect[0]
tmp = operate_on_Narray(cont_pos_vect[0], tmp[a + 1], lambda x, y: x + y)
tmp = operate_on_Narray(cont_pos_vect[0], tmp[a + 1], lambda x, y: x / len1)
cont_pos_vect = list(tmp)
else:
cont_pos_vect = []
############################################ 7
len1 = len(neut_neu_vect)
if len1 != 0:
for a in range(len1):
tmp = neut_neu_vect[0]
tmp = operate_on_Narray(neut_neu_vect[0], tmp[a + 1], lambda x, y: x + y)
tmp = operate_on_Narray(neut_neu_vect[0], tmp[a + 1], lambda x, y: x / len1)
neut_neu_vect = list(tmp)
else:
neut_neu_vect = []
############################################ 8
len1 = len(neut_neg_vect)
if len1 != 0:
for a in range(len1):
tmp = neut_neg_vect[0]
tmp = operate_on_Narray(neut_neg_vect[0], tmp[a + 1], lambda x, y: x + y)
tmp = operate_on_Narray(neut_neg_vect[0], tmp[a + 1], lambda x, y: x / len1)
neut_neg_vect = list(tmp)
else:
neut_neg_vect = []
############################################ 9
len1 = len(neut_pos_vect)
if len1 != 0:
for a in range(len1):
tmp = neut_pos_vect[0]
tmp = operate_on_Narray(neut_pos_vect[0], tmp[a + 1], lambda x, y: x + y)
tmp = operate_on_Narray(neut_pos_vect[0], tmp[a + 1], lambda x, y: x / len1)
neut_pos_vect = list(tmp)
else:
neut_pos_vect = []
return [part_neu_vect, part_neg_vect, part_pos_vect, cont_neu_vect, cont_neg_vect, cont_pos_vect, neut_neu_vect, neut_neg_vect, neut_pos_vect]
def saveTraining():
sert = trainVect()
trnVect = open('trn_vect.vec', 'w')
for i in sert:
trnVect.write(str(i) + '\n')

45
retEmoDict.py
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@ -1,45 +0,0 @@
def emoDic():
emoDict = open('SEL.txt', 'r', encoding='utf-8')
temp = emoDict.read()
emoDict = temp.split('\n')
temp = []
for i in emoDict:
temp.append(i.split('\t'))
n = len(temp) -1
del temp[n]
for i in temp:
del i[1]
emoDict = {i[0]: i[1] for i in temp}
emoDict['PRI'] = 'Positivo'
emoDict['INE'] = 'Neutro'
emoDict['electoral'] = 'Neutro'
emoDict['Electoral'] = 'Neutro'
emoDict['PAN'] = 'Negativo'
emoDict['partido'] = 'Neutro'
emoDict['Partido'] = 'Neutro'
emoDict['PRD'] = 'Negativo'
emoDict['PT'] = 'Negativo'
emoDict['PANAL'] = 'Negativo'
emoDict['PVEM'] = 'Negativo'
emoDict['Movimiento'] = 'Negativo'
emoDict['Ciudadano'] = 'Negativo'
emoDict['Alianza'] = 'Negativo'
emoDict['Morena'] = 'Negtivo'
emoDict['electoral'] = 'Neutro'
emoDict['Electoral'] = 'Neutro'
emoDict['Encuentro'] = 'Negativo'
emoDict['Social'] = 'Negativo'
emoDict['Peña'] = 'Positivo'
emoDict['Nieto'] = 'Sorpresa' #['Sorpresa', 'Positivo']
return emoDict

45
similarityMeasures.py
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@ -1,45 +0,0 @@
"""
Created on Mon Apr 17 09:34:40 2017
functions to calculate the similarity measure of two real vectors
@author: nlp
"""
# The cosine measure definition
def cos_sim(vect1, vect2):
if (len(vect1) == len(vect2)):
vect3 = []
for x in range(0, len(vect1)):
vect3.append(0)
for x in range(0, len(vect1)):
vect3[x] = vect1[x] * vect2[x]
n1 = norm(vect1)
n2 = norm(vect2)
return sum(vect3)/(n1*n2)
else:
return 0
# Norm of vector
def norm(vect):
import math as mth
vect1 = []
for x in range(0, len(vect)):
vect1.append(0)
for x in range(0, len(vect)):
vect1[x] = vect[x] * vect[x]
return mth.sqrt(sum(vect1))
# Jacard similarity
def jac_sim(set_A,set_B):
if (str(type(set_A)) and str(type(set_B))) == "<class 'set'>":
if set_A == set_B:
return len(set_A & set_B)/len(set_A | set_B)
else:
return len(set_A & set_B)/len((set_A | set_B) - (set_A & set_B))
else:
print('One of the inputs not of type set')

7
stopWords.py
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@ -1,7 +0,0 @@
def stopWrdList():
sw = open('stop.words')
prue = []
prue.append(sw.readlines())
return [l.strip('\n\r') for l in prue[0]]

9
trn_vect.vec
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@ -1,9 +0,0 @@
[0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.049274784954524135, 0.0, 0.052802642179678255, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.14190845147296166, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.093751910664710822, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.12260657382584206, 0.0, 0.1792195920876137, 0.0, 0.0, 0.05863903838975032, 0.0, 0.0, 0.078987206235014609, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.14190845147296166, 0.0]
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