v0.31

Fedora25 work

.idea/vcs.xml

@@ -0,0 +1,6 @@
+<?xml version="1.0" encoding="UTF-8"?>
+<project version="4">
+  <component name="VcsDirectoryMappings">
+    <mapping directory="$PROJECT_DIR$" vcs="Git" />
+  </component>
+</project>

classify_news.py

@@ -0,0 +1,80 @@
+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])

clust.py

@@ -1,45 +1,49 @@
 from infBack import get_vect as gv
 from sklearn.feature_extraction.text import TfidfVectorizer
+from stopWords import stopWrdList
 import numpy as np
 
 
-def stopWrdList():
+def clustering():
-    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"]
 
-voc = ["ine", "pri", "pan", "prd", "pt", "pvem", "verde", "movimiento", "ciudadano", "panal", "alianza", "morena", "partido", "encuentro", "social", "electoral"]
+    stop_words = stopWrdList()
 
-stop_words = stopWrdList()
+    dataVect = gv()
 
-dataVect = gv()
+    dataVect = np.array(dataVect)
 
-dataVect = np.array(dataVect)
+    corpus = dataVect[:, 2]
 
-corpus = dataVect[:, 2]
+    vectorizer = TfidfVectorizer(strip_accents='ascii', analyzer='word', stop_words=stop_words, vocabulary=voc)
 
-vectorizer = TfidfVectorizer(strip_accents='ascii', analyzer='word', stop_words=stop_words, vocabulary=voc)
+    X = vectorizer.fit_transform(corpus)
 
-X = vectorizer.fit_transform(corpus)
+    del dataVect, stop_words, vectorizer  # , corpus
 
-del dataVect, stop_words, vectorizer  # , corpus
+    J = X.toarray()
 
-J = X.toarray()
+    # The indexes are extracted to obtain only the relevant news from the general corpus
 
-# print(J)
+    index = []
 
-index = []
+    for x in range(0, len(J)):
-
-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
 
-print(electCorp)
+    # 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

infBack.py

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

main.py

@@ -0,0 +1,8 @@
+
+# from newsTrain import saveTraining
+from classify_news import classify_news
+
+# saveTraining()
+
+classify_news('news_to_classify.txt')
+classify_news('news2.txt')

newsTrain.py

@@ -0,0 +1,382 @@
+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')

retEmoDict.py

@@ -0,0 +1,45 @@
+
+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

similarityMeasures.py

@@ -0,0 +1,45 @@
+"""
+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')
+        

stopWords.py

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

trn_vect.vec

@@ -0,0 +1,9 @@
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