Now System is working based on customized dic and also SentiWordNet

InputTxt.py

@@ -1,5 +1,6 @@
-text = """What can I say about this place. The staff of the restaurant is 
-    nice and the eggplant is not bad. Apart from that, very uninspired food, 
-    lack of atmosphere and too expensive. I am a staunch vegetarian and was 
-    sorely disappointed with the veggie options on the menu. Will be the last 
-    time I visit, I recommend others to avoid."""
+text = "This toy does not look like the picture at all." \
+       " The bread, tomato, and lettuce were all hard. " \
+       "The only parts that are a bit realistic are the cheese and meats." \
+       " This toy came with only one tomato and one lettuce." \
+       " That's not enough objects to make multiple full sandwiches." \
+       " I would recommend not buying this toy if you want your money's worth."

SA.py → SACustomizedDic.py

@@ -1,7 +1,7 @@
 import nltk
 import InputTxt
 import yaml
-from nltk.corpus import sentiwordnet as swn
+
 
 
 class PreProcess(object):
@@ -119,14 +119,7 @@ def sa_score(self, dictionary_tagged_sentence):
 print(POS)
 print("Adjust with the Text Structure ......")
 print(Adjust)
-Option = input("Select the mode of tagging tokens:\n 1.Customized Dictionaries\n 2.SentiWordNet3.0\n 3.SentiPers\n")
-if Option == "1":
-    print("Tagging the tokens with Dictionaries ......")
-    print(dict_tagged_sentences)
-if Option == "2":
-    print("Tagging the tokens with SentiWordNet")
-if Option == "3":
-    print("Tagging the tokens with Sentipers")
-
+print("Tagging the tokens with Dictionaries ......")
+print(dict_tagged_sentences)
 print("..... Sentiment Analysing .....")
 print("AVG:", Score)

SASentiWordNet.py

@@ -0,0 +1,51 @@
+import nltk
+from nltk.corpus import sentiwordnet as swn
+import InputTxt
+
+sentences = nltk.sent_tokenize(InputTxt.text)
+print(sentences)
+sentences_tokens = [nltk.word_tokenize(s) for s in sentences]
+print(sentences_tokens)
+tagged_list = []
+for i in sentences_tokens:
+    tagged_list.append(nltk.pos_tag(i))
+print(tagged_list)
+wordNetLemmatizer = nltk.WordNetLemmatizer()
+
+score_list = []
+for i, j in enumerate(tagged_list):
+    score_list.append([])
+    for m, n in enumerate(j):
+        new_tag = ''
+        lemmatized = wordNetLemmatizer.lemmatize(n[0])
+        if n[1].startswith('NN'):
+            new_tag = 'n'
+        elif n[1].startswith('JJ'):
+            new_tag = 'a'
+        elif n[1].startswith('V'):
+            new_tag = 'v'
+        elif n[1].startswith('R'):
+            new_tag = 'r'
+        else:
+            new_tag = ''
+        if new_tag != '':
+            synsets = list(swn.senti_synsets(lemmatized, new_tag))
+            score = 0
+            if len(synsets) > 0:
+                for s in synsets:
+                    score += s.pos_score() - s.neg_score()
+                score_list[i].append(score/len(synsets))
+
+print(score_list)
+sentences_sentiment = []
+
+for s in score_list:
+    sentences_sentiment.append(sum([w for w in s])/len(s))
+print("Final Sentiment Analysis for each sentences in Input text: .........")
+print(sentences_sentiment)
+
+
+
+
+
+