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Sentiment_Analyzer.py
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Sentiment_Analyzer.py
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import os,glob,re
from string import punctuation
from collections import defaultdict
from collections import Counter
import numpy as np
from collections import OrderedDict
import copy
from sklearn import metrics
import operator
from textblob.classifiers import NaiveBayesClassifier
list_negative_word = []
list_positive_word = []
Unique_list_positive_word = []
Unique_list_negative_word = []
cwd = os.getcwd()
count_positive_ID = 0
count_negative_ID = 0
Negative_Word_Dict = defaultdict()
Positive_Word_Dict = defaultdict()
Sentence_Sentiment = {}
Evaluation_Dict = OrderedDict()
frequent_word_list = ['ourselves', 'hers', 'between', 'yourself', 'but', 'again', 'there', 'about', 'once', 'during', 'out', 'very', 'having', 'with', 'they', 'own', 'an', 'be', 'some', 'for', 'do', 'its', 'yours', 'such', 'into', 'of', 'most', 'itself', 'other', 'off', 'is', 's', 'am', 'or', 'who', 'as', 'from', 'him', 'each', 'the', 'themselves', 'until', 'below', 'are', 'we', 'these', 'your', 'his', 'through', 'don', 'nor', 'me', 'were', 'her', 'more', 'himself', 'this', 'down', 'should', 'our', 'their', 'while', 'above', 'both', 'up', 'to', 'ours', 'had', 'she', 'all', 'no', 'when', 'at', 'any', 'before', 'them', 'same', 'and', 'been', 'have', 'in', 'will', 'on', 'does', 'yourselves', 'then', 'that', 'because', 'what', 'over', 'why', 'so', 'can', 'did', 'not', 'now', 'under', 'he', 'you', 'herself', 'has', 'just', 'where', 'too', 'only', 'myself', 'which', 'those', 'i', 'after', 'few', 'whom', 't', 'being', 'if', 'theirs', 'my', 'against', 'a', 'by', 'doing', 'it', 'how', 'further', 'was', 'here', 'than']
ID_Val = []
#Basic data cleaning using regular expressions. I have removed the frequently repeated words
#as described in the above list. I have eliminated all kind of punctuation marks - like .,!,-, etc
for line in open("hotelNegT-train.txt",encoding="utf8").readlines():
if line.strip():
word_split = line.replace(',',' ').replace('.',' ').replace('!',' ').replace('--',' ').split()
for each_word in word_split:
if re.match(r'[IDidIdiD].*-[0-9].*',each_word):
count_negative_ID+=1
ID_Val.append(each_word)
continue
elif re.match(r'(\d+)\.(\d+)+',each_word):
continue
elif re.match(r'(\d+)+',each_word):
continue
elif re.match(r'$(\d+)+',each_word):
continue
elif re.match(r'[A-Za-z]*\.+',each_word):
each_word = re.sub(r'[^\w\s]','',each_word)
elif re.match(r'[A-Za-z]*\?+',each_word):
each_word = re.sub(r'[^\w\s]','',each_word)
elif re.match(r'[A-Za-z]*\!+',each_word):
each_word = re.sub(r'[^\w\s]','',each_word)
elif re.match(r'-+',each_word):
continue
elif re.match(r'[A-Za-z]*\)+',each_word):
each_word = re.sub(r'[^\w\s]','',each_word)
elif re.match(r'\(+[A-Za-z]*',each_word):
each_word = re.sub(r'[^\w\s]','',each_word)
elif re.match(r'[A-Za-z]*\,+',each_word):
each_word = re.sub(r'[^\w\s]','',each_word)
if each_word in frequent_word_list:
continue
else:
list_negative_word.append(each_word)
List_Size_Negative = len(list_negative_word)
Negative_Word_Dict = Counter(list_negative_word)
Denominator_Sum_Neg = sum(Negative_Word_Dict.values())
Unique_list_negative_word = set(list_negative_word)
Unique_List_Size_Negative = len(Unique_list_negative_word)
print (ID_Val)
list_positive_word = []
for line in open("hotelPosT-train.txt",encoding="utf8").readlines():
if line.strip():
word_split = line.split()
word_split = line.replace(',',' ').replace('.',' ').replace('!',' ').replace('--',' ').split()
for each_word in word_split:
if re.match(r'ID-[0-9].*',each_word):
count_positive_ID+=1
ID_Val = each_word
continue
elif re.match(r'(\d+)\.(\d+)+',each_word):
continue
elif re.match(r'(\d+)+',each_word):
continue
elif re.match(r'$(\d+)+',each_word):
continue
elif re.match(r'[A-Za-z]*\.+',each_word):
each_word = re.sub(r'[^\w\s]','',each_word)
elif re.match(r'[A-Za-z]*\?+',each_word):
each_word = re.sub(r'[^\w\s]','',each_word)
elif re.match(r'[A-Za-z]*\!+',each_word):
each_word = re.sub(r'[^\w\s]','',each_word)
elif re.match(r'-+',each_word):
continue
elif re.match(r'[A-Za-z]*\)+',each_word):
each_word = re.sub(r'[^\w\s]','',each_word)
elif re.match(r'\(+[A-Za-z]*',each_word):
each_word = re.sub(r'[^\w\s]','',each_word)
elif re.match(r'[A-Za-z]*\,+',each_word):
each_word = re.sub(r'[^\w\s]','',each_word)
elif re.match(r'[A-Za-z]*\/+[A-Za-z]*',each_word):
list_word = each_word.split('/')
if list_word[0] not in frequent_word_list:
list_negative_word.append(list_word [0])
else:
continue
if list_word[1] not in frequent_word_list:
list_negative_word.append(list_word [1])
else:
continue
if each_word in frequent_word_list:
continue
else:
list_positive_word.append(each_word)
#List of all words in positive training set with repitition
List_Size_Positive = len(list_positive_word)
#Dictionary having the words : count
Positive_Word_Dict = Counter(list_positive_word)
Denominator_Sum_Pos = sum(Positive_Word_Dict.values())
#List of all unique words in Positive Training set
Unique_list_positive_word = set(list_positive_word)
#Length of Unique Size list
Unique_List_Size_Positive = len(Unique_list_positive_word)
Total_No_of_Docs = count_positive_ID + count_negative_ID
print ("count_positive_ID - ",count_positive_ID)
print ("count_negative_ID - ",count_negative_ID)
print ("Total_No_of_Docs - ",Total_No_of_Docs)
Log_Prior_Positive = np.log10(float(count_positive_ID)/float(Total_No_of_Docs))
Log_Prior_Negative = np.log10(float(count_negative_ID)/float(Total_No_of_Docs))
print ("Log_Prior_Positive - ",Log_Prior_Positive)
print ("Log_Prior_Negative - ",Log_Prior_Negative)
Negative_Word_Dict_final = defaultdict()
Positive_Word_Dict_final = defaultdict()
print (Denominator_Sum_Pos)
print (Denominator_Sum_Neg)
#Creating maximum likelihood estimate values for each word in positive training set
for key in Positive_Word_Dict:
val = np.log10 (float(Positive_Word_Dict[key] + 1)/float(Denominator_Sum_Pos + Unique_List_Size_Positive))
Positive_Word_Dict_final.update({key:val})
for key in Negative_Word_Dict:
val = np.log10(float(Negative_Word_Dict[key] + 1)/float(Denominator_Sum_Neg + Unique_List_Size_Negative))
Negative_Word_Dict_final.update({key:val})
Negative_Probability_Sentence = 0.00
Positive_Probability_Sentence = 0.00
Sentence_Token_List = []
Sentence_List = []
with open("TestSet.txt",'r',encoding="utf8") as f:
token = [line.split() for line in f]
for each_word in token:
Sentence_Token_List.append(each_word)
Test_Review_Dict = OrderedDict()
Test_Review_Class = OrderedDict()
temp_dict = {}
List_Tokens_ID = []
temp = []
for i in range (0,len(Sentence_Token_List)):
for j in range (0,len(Sentence_Token_List[i])):
word = Sentence_Token_List[i][j]
if re.match(r'ID-[0-9].*',word):
Id_Value = word
continue
elif re.match(r'(\d+)\.(\d+)+',word):
continue
elif re.match(r'(\d+)+',word):
continue
elif re.match(r'$(\d+)+',word):
continue
elif re.match(r'[A-Za-z]*\.+',word):
word = re.sub(r'[^\w\s]','',word)
elif re.match(r'[A-Za-z]*\?+',word):
word = re.sub(r'[^\w\s]','',word)
elif re.match(r'[A-Za-z]*\!+',word):
word = re.sub(r'[^\w\s]','',word)
elif re.match(r'-+',word):
continue
elif re.match(r'[A-Za-z]*\)+',word):
word = re.sub(r'[^\w\s]','',word)
elif re.match(r'\(+[A-Za-z]*',word):
word = re.sub(r'[^\w\s]','',word)
elif re.match(r'[A-Za-z]*\,+',word):
word = re.sub(r'[^\w\s]','',word)
elif re.match(r'[A-Za-z]*\/+[A-Za-z]*',word):
list_word = word.split('/')
if list_word[0] in frequent_word_list:
continue
else:
List_Tokens_ID.append(list_word[0])
if list_word[1] in frequent_word_list:
continue
else:
List_Tokens_ID.append(list_word[0])
if (word not in frequent_word_list):
List_Tokens_ID.append(word)
temp = copy.copy(List_Tokens_ID)
Test_Review_Dict.update ({Id_Value:temp})
List_Tokens_ID[:] = []
for each_key,each_value in Test_Review_Dict.items():
ID_Value = each_key
for val in each_value:
if ((val in Negative_Word_Dict_final) and (val in Positive_Word_Dict_final)):
Negative_Probability_Sentence += Negative_Word_Dict_final[val]
Positive_Probability_Sentence += Positive_Word_Dict_final[val]
Positive_Probability_Sentence+=Log_Prior_Positive
Negative_Probability_Sentence+=Log_Prior_Negative
temp_dict.update({'POS': Positive_Probability_Sentence})
temp_dict.update({'NEG': Negative_Probability_Sentence})
print ("ID- Value : {}, Negative : {} , Positive : {}".format(ID_Value, Negative_Probability_Sentence,Positive_Probability_Sentence))
key = [k for k,v in temp_dict.items() if v==max(temp_dict.values())][0]
Test_Review_Class.update ({ID_Value:key})
temp_dict.clear()
Positive_Probability_Sentence = 0.0
Negative_Probability_Sentence = 0.0
with open("Upadhyaya-Swathi-assgn3-out_BasicNaiveBayes.txt",'w') as ofile:
for keys,values in Test_Review_Class.items():
ofile.write((str(keys) + '\t' + values + '\n'))
####with open("Evaluation.txt",'w') as ofile:
#### for keys,values in Evaluation_Dict.items():
#### ofile.write((str(keys) + '\t' + values + '\n'))
##wrong_ans = 0
##for line in open('Evaluation.txt','r').readlines():
## if line.strip():
## word_split = line.split()
## Evaluation_Dict.update({word_split[0] : word_split[1]})
##
##for key in Test_Review_Class.keys():
## if key in Evaluation_Dict.keys():
## count+=1
## if Evaluation_Dict[key] == Test_Review_Class[key]:
## accuracy+=1
## else:
## #print ("TAG DONT MATCH!")
## wrong_ans+=1
## else:
## print ("Key NOT Found!")
##print (float(accuracy)/float(count))
##print ("Wrong Answers - ",wrong_ans)
def NaiveBayes_BuiltIn_Package():
Train_Set_NEG = []
count_neg = 0
Training_Set = []
ID_val_List = []
for line in open("hotelNegT-train.txt",'r',encoding="utf8").readlines():
if re.match(r'ID-[0-9].*',line):
count_neg+=1
each_line = re.sub(r'ID-[0-9].*_','',line)
each_line = each_line.strip("\n")
Train_Set_NEG.append(each_line[8:])
for each_sentence in Train_Set_NEG:
Training_Set.append((each_sentence,'NEG'))
Train_Set_POS = []
count_pos = 0
for line in open("hotelPosT-train.txt",'r',encoding="utf8").readlines():
if re.match(r'ID-[0-9].*',line):
count_pos+=1
each_line = re.sub(r'ID-[0-9].*_','',line)
each_line = each_line.strip()
Train_Set_POS.append(each_line[8:])
for each_sentence in Train_Set_POS:
Training_Set.append((each_sentence,'POS'))
classifier = NaiveBayesClassifier(Training_Set)
Test_Set = []
for line in open("TestSet.txt",'r',encoding="utf8").readlines():
if re.match(r'ID-[0-9].*',line):
each_line = re.sub(r'ID-[0-9].*_','',line)
each_line = each_line.strip()
Test_Set.append(each_line[8:])
ID_val_List.append(each_line[0:7])
sol_list = []
for each_item in Test_Set:
sol_list.append (classifier.classify(each_item))
dictionary = OrderedDict()
dictionary = dict(zip(ID_val_List, sol_list))
with open("Output.txt",'w') as ofile:
for key,val in dictionary.items():
ofile.write((str(key) + '\t' + str(val) + '\n'))
NaiveBayes_BuiltIn_Package()