v0.2

main.py

@@ -1,19 +1,8 @@
 
-from retEmoDict import emoDic
+from newsTrain import trainVect, flagger
-from newsTrain import classifyNews
-from clust import clustering
 
 
-temp = clustering()
+sert = trainVect()
 
-emoDict = emoDic()
+for i in sert:
-
-rest = []
-
-for i in temp:
-    rest.append(classifyNews(i, emoDict))
-
-
-for i in rest:
     print(i)
-

newsTrain.py

@@ -1,6 +1,10 @@
+from sklearn.feature_extraction.text import TfidfVectorizer
+from stopWords import stopWrdList
+from retEmoDict import emoDic
+from clust import clustering
+import operator
 
-
+def trainPre(word_array, dict):
-def classifyNews(word_array, dict):
 
     default = 'NA'
     alegria = []
@@ -72,6 +76,268 @@ def classifyNews(word_array, dict):
     if len(flag) == 0:
         flag = ['NEU']
 
-    return [('Positive:', pos), ('Negative:', neg), flag, vect]
+    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)
+
+    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]
+
+    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 = []
+
+    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 = []
+
+    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 = []
+
+    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 = []
+
+    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 = []
+
+    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 = []
+
+    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 = []
+
+    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]
+