count += ALPHA
total += vocabulary_size*ALPHA
#End Smoothing
return float(count)/total
def total(bag):
total = 0
#return the total count of words in a bag
for value in bag.values():
total += value
return total;
####################################
#Step 1: Get the distinct tokens in the training data and the total number of tokens a.k.a bag-of-words
wordTokenizer = WordTokenizer()
words_in_training = wordTokenizer.tokenizeDirectoryAsDictionary(TRAINING_DATA_DIRECTORY)
training_document_bag_dictionary = {}
for key, token_list in words_in_training.iteritems():
bag_of_words = defaultdict(int)
for word in token_list:
bag_of_words[word]+=1
training_document_bag_dictionary[key] = bag_of_words
training_document_bag_dictionary = OrderedDict(sorted(training_document_bag_dictionary.items(), key=lambda t: t[0]))
#print training_document_bag_dictionary
positive_training_document_bag_dictionary = {}
negative_training_document_bag_dictionary = {}
objective_training_document_bag_dictionary = {}
#######Constants###########
POSITIVE_POLARITY = "positive"
NEGATIVE_POLARITY = "negative"
OBJECTIVE_POLARITY = "neutral"
POSITIVE_POLARITY_FOR_SCORER = "POS"
NEGATIVE_POLARITY_FOR_SCORER = "NEG"
OBJECTIVE_POLARITY_FOR_SCORER = "OBJ"
GENERATED_FILES_DIRECTORY = "../../generated_files/"
tuning_parameter = 6
#####End of Constants#####
classificationSentimentDictionary = SentimentParser().wordsClassification()
#print classificationSentimentDictionary
tokenizer = WordTokenizer()
fileDictionary = tokenizer.tokenizeDirectoryAsDictionary("dev")
fileDictionary = OrderedDict(sorted(fileDictionary.items(), key=lambda t: t[0]))
fileClassDictionary = {}
for key, value_as_list in fileDictionary.iteritems():
positive = 0
negative = 0
for word in value_as_list:
if word in classificationSentimentDictionary:
word_sense_list = classificationSentimentDictionary[word]
word_sense = word_sense_list[random.randint(0, len(word_sense_list)-1)]
if word_sense == POSITIVE_POLARITY:
positive+=1
elif word_sense == NEGATIVE_POLARITY:
negative+=1
abstotal = abs(positive - negative)
def minmaxSmoothing(power):
alpha = math.pow(10, power)
#Step 1: Get the distinct tokens in the training data and the total number of tokens a.k.a bag-of-words
wordTokenizer = WordTokenizer()
words_in_training = wordTokenizer.tokenizeDirectoryAsDictionary(TRAINING_DATA_DIRECTORY)
training_document_bag_dictionary = {}
for key, token_list in words_in_training.iteritems():
bag_of_words = defaultdict(int)
for word in token_list:
bag_of_words[word]+=1
training_document_bag_dictionary[key] = bag_of_words
training_document_bag_dictionary = OrderedDict(sorted(training_document_bag_dictionary.items(), key=lambda t: t[0]))
#print training_document_bag_dictionary
positive_training_document_bag_dictionary = {}
negative_training_document_bag_dictionary = {}
objective_training_document_bag_dictionary = {}
total_positive = 0
total_negative = 0
total_objective = 0
for line in fileinput.input([TRAIN_KEY_FILE]):
temp_line = line.split(" ")
fileName = temp_line[0]
sense = temp_line[1].strip()
fileName = "../../"+fileName #Make it into a suitable format
if sense == POSITIVE_POLARITY_FOR_SCORER: #document is a positive one
#Add the fileName -> bag-of-words(fileName) to a dict
positive_training_document_bag_dictionary[fileName] = training_document_bag_dictionary[fileName]
total_positive += total(training_document_bag_dictionary[fileName])
elif sense == NEGATIVE_POLARITY_FOR_SCORER:
negative_training_document_bag_dictionary[fileName] = training_document_bag_dictionary[fileName]
total_negative += total(training_document_bag_dictionary[fileName])
elif sense == OBJECTIVE_POLARITY_FOR_SCORER:
objective_training_document_bag_dictionary[fileName] = training_document_bag_dictionary[fileName]
total_objective += total(training_document_bag_dictionary[fileName])
training_total_documents = len(training_document_bag_dictionary)
training_total_positive_documents = len(positive_training_document_bag_dictionary)
training_total_negative_documents = len(negative_training_document_bag_dictionary)
training_total_objective_documents = len(objective_training_document_bag_dictionary)
probability_positive = float(training_total_positive_documents)/ training_total_documents
probability_negative = float(training_total_negative_documents)/ training_total_documents
probability_objective = float(training_total_objective_documents)/ training_total_documents
log_positive = math.log(probability_positive)
log_negative = math.log(probability_negative)
log_objective = math.log(probability_objective)
words_in_development = wordTokenizer.tokenizeDirectoryAsDictionary(DEVELOPMENT_DATA_DIRECTORY)
words_in_development = OrderedDict(sorted(words_in_development.items(), key=lambda t: t[0]))
total_count = total_positive + total_negative + total_objective
responseFile = GENERATED_FILES_DIRECTORY+"smoothing_response_MIN_MAX"+str(power)
f = open(responseFile, "w")
for key, token_list in words_in_development.iteritems():
sentiment = ""
p = 0
n = 0
o = 0
#Step 2: P(POS|Document) P(NEG|Document} P(OBJ|Document)
for word in token_list:
#Step 2.1
#P(k| POS) P(k| NEG) P(k| OBJ) for each word in this document
probability_word_positive_documents = probabilityOfWordInSeveralDocumentsPutTogether(word, positive_training_document_bag_dictionary, total_count, alpha)
log_probability_positive = math.log(probability_word_positive_documents) if probability_word_positive_documents > 0 else NEG_INFINITY
p+=log_probability_positive
probability_word_negative_documents = probabilityOfWordInSeveralDocumentsPutTogether(word, negative_training_document_bag_dictionary, total_count, alpha)
log_probability_negative = math.log(probability_word_negative_documents) if probability_word_negative_documents > 0 else NEG_INFINITY
n+=log_probability_negative
probability_word_objective_documents = probabilityOfWordInSeveralDocumentsPutTogether(word, objective_training_document_bag_dictionary, total_count, alpha)
log_probability_objective = math.log(probability_word_objective_documents) if probability_word_objective_documents > 0 else NEG_INFINITY
o+=log_probability_objective
#Step 2.2: P(C|Document) = P(C) + all the P(k|C) --> Here in log form
p+=log_positive
n+=log_negative
o+=log_objective
if p > n:
if p > o:
sentiment = POSITIVE_POLARITY_FOR_SCORER
theta = p
else:
sentiment = OBJECTIVE_POLARITY_FOR_SCORER
theta = o
else:
if n > o:
sentiment = NEGATIVE_POLARITY_FOR_SCORER
theta = n
else:
sentiment = OBJECTIVE_POLARITY_FOR_SCORER
theta = o
print "Document Name: "+key+" is classified as: "+sentiment
f.write(sentiment+"\t"+str(theta)+"\n")
f.close()
count += ALPHA
total += vocabulary_size*ALPHA
#End Smoothing
return float(count)/total
def total(bag):
total = 0
#return the total count of words in a bag
for value in bag.values():
total += value
return total;
####################################
#Step 1: Get the distinct tokens in the training data and the total number of tokens a.k.a bag-of-words
wordTokenizer = WordTokenizer()
words_in_training = wordTokenizer.tokenizeDirectoryAsDictionary(TRAINING_DATA_DIRECTORY)
words_in_training = OrderedDict(sorted(words_in_training.items(), key=lambda t: t[0]))
training_document_bag_dictionary = {}
for key, token_list in words_in_training.iteritems():
bag_of_words = defaultdict(int)
for word in token_list:
bag_of_words[word]+=1
training_document_bag_dictionary[key] = bag_of_words
training_document_bag_dictionary = OrderedDict(sorted(training_document_bag_dictionary.items(), key=lambda t: t[0]))
#print training_document_bag_dictionary
positive_training_document_bag_dictionary = {}
negative_training_document_bag_dictionary = {}
#######Constants###########
POSITIVE_POLARITY = "positive"
NEGATIVE_POLARITY = "negative"
OBJECTIVE_POLARITY = "neutral"
POSITIVE_POLARITY_FOR_SCORER = "POS"
NEGATIVE_POLARITY_FOR_SCORER = "NEG"
OBJECTIVE_POLARITY_FOR_SCORER = "OBJ"
GENERATED_FILES_DIRECTORY = "../../generated_files/"
tuning_parameter = 10
#####End of Constants#####
classificationSentimentDictionary = SentimentParser().wordsClassification()
#print classificationSentimentDictionary
tokenizer = WordTokenizer()
fileDictionary = tokenizer.tokenizeDirectoryAsDictionary("train")
fileDictionary = OrderedDict(sorted(fileDictionary.items(), key=lambda t: t[0]))
fileClassDictionary = {}
for key, value_as_list in fileDictionary.iteritems():
positive = 0
negative = 0
for word in value_as_list:
if word in classificationSentimentDictionary:
word_sense_list = classificationSentimentDictionary[word]
word_sense = word_sense_list[random.randint(0, len(word_sense_list)-1)]
if word_sense == POSITIVE_POLARITY:
positive+=1
elif word_sense == NEGATIVE_POLARITY:
negative+=1
abstotal = abs(positive - negative)
