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CreateTestInput.py
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CreateTestInput.py
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from pprint import pprint
import json
import csv
import numpy as np
from pprint import pprint
import nltk
import yaml
import sys
import os
import re
class Splitter(object):
def __init__(self):
self.nltk_splitter = nltk.data.load('tokenizers/punkt/english.pickle')
self.nltk_tokenizer = nltk.tokenize.TreebankWordTokenizer()
def split(self, text):
sentences = self.nltk_splitter.tokenize(text)
tokenized_sentences = [self.nltk_tokenizer.tokenize(sent) for sent in sentences]
return tokenized_sentences
class POSTagger(object):
def __init__(self):
pass
def pos_tag(self, sentences):
pos = [nltk.pos_tag(sentence) for sentence in sentences]
#adapt format
pos = [[(word, word, [postag]) for (word, postag) in sentence] for sentence in pos]
return pos
class DictionaryTagger(object):
def __init__(self, dictionary_paths):
files = [open(path, 'r') for path in dictionary_paths]
dictionaries = [yaml.load(dict_file) for dict_file in files]
map(lambda x: x.close(), files)
self.dictionary = {}
self.max_key_size = 0
for curr_dict in dictionaries:
for key in curr_dict:
if key in self.dictionary:
self.dictionary[key].extend(curr_dict[key])
else:
self.dictionary[key] = curr_dict[key]
self.max_key_size = max(self.max_key_size, len(key))
def tag(self, postagged_sentences):
return [self.tag_sentence(sentence) for sentence in postagged_sentences]
def tag_sentence(self, sentence, tag_with_lemmas=False):
tag_sentence = []
N = len(sentence)
if self.max_key_size == 0:
self.max_key_size = N
i = 0
while (i < N):
j = min(i + self.max_key_size, N) #avoid overflow
tagged = False
while (j > i):
expression_form = ' '.join([word[0] for word in sentence[i:j]]).lower()
expression_lemma = ' '.join([word[1] for word in sentence[i:j]]).lower()
if tag_with_lemmas:
literal = expression_lemma
else:
literal = expression_form
if literal in self.dictionary:
#self.logger.debug("found: %s" % literal)
is_single_token = j - i == 1
original_position = i
i = j
taggings = [tag for tag in self.dictionary[literal]]
tagged_expression = (expression_form, expression_lemma, taggings)
if is_single_token: #if the tagged literal is a single token, conserve its previous taggings:
original_token_tagging = sentence[original_position][2]
tagged_expression[2].extend(original_token_tagging)
tag_sentence.append(tagged_expression)
tagged = True
else:
j = j - 1
if not tagged:
tag_sentence.append(sentence[i])
i += 1
return tag_sentence
def value_of(sentiment):
if sentiment == 'positive': return -1
if sentiment == 'negative': return 1
return 0
def value_of_threat(sentiment):
if sentiment == 'positive': return -1
if sentiment == 'negative': return 1
if sentiment == 'threat': return 1
return 0
def sentence_score(sentence_tokens, previous_token, acum_score):
if not sentence_tokens:
return acum_score
else:
current_token = sentence_tokens[0]
tags = current_token[2]
token_score = sum([value_of(tag) for tag in tags])
if previous_token is not None:
previous_tags = previous_token[2]
if 'inc' in previous_tags:
token_score *= 2.0
elif 'dec' in previous_tags:
token_score /= 2.0
elif 'inv' in previous_tags:
token_score *= -1.0
return sentence_score(sentence_tokens[1:], current_token, acum_score + token_score)
def threatening_score(sentence_tokens, previous_token, acum_score):
if not sentence_tokens:
return acum_score
else:
current_token = sentence_tokens[0]
tags = current_token[2]
token_score = sum([value_of_threat(tag) for tag in tags])
if previous_token is not None:
previous_tags = previous_token[2]
if 'inc' in previous_tags:
token_score *= 2.0
elif 'dec' in previous_tags:
token_score /= 2.0
elif 'inv' in previous_tags:
token_score *= -1.0
return threatening_score(sentence_tokens[1:], current_token, acum_score + token_score)
def threat_score(review):
return sum([threatening_score(sentence, None, 0.0) for sentence in review])
def sentiment_score(review):
return sum([sentence_score(sentence, None, 0.0) for sentence in review])
if __name__ == "__main__":
with open ('testinput.txt','w') as f:
with open("testdata.txt") as file:
reader = csv.reader(file)
for row in reader:
#print(row)
text = ''.join(row)
new_text = text.replace(',', '')
new_text = new_text.replace('.', '')
#pprint(new_text)
output_json = json.load(open('threat.json'))
threat_scores = 0.0
number_of_threat = 0
for word in new_text.split():
for majorkey, subdict in output_json.iteritems():
if word == majorkey:
#print(subdict)
threat_scores+=float(subdict)
number_of_threat+=1
#pprint(threat_score)
#pprint(number_of_threat)
charLength = float(len(text))
wordLength = float(len(text.split()))
averages = float(charLength/wordLength)
#pprint('Character Length-> %d'%charLength)
#pprint('Word Length-> %d'%wordLength)
splitter = Splitter()
postagger = POSTagger()
dicttagger = DictionaryTagger([ 'Dicts/threatWords.yml','Dicts/Positive.yml', 'Dicts/Negative.yml',
'Dicts/Increasing.yml', 'Dicts/Decreasing.yml', 'Dicts/Inverting.yml'])
splitted_sentences = splitter.split(text)
pos_tagged_sentences = postagger.pos_tag(splitted_sentences)
dict_tagged_sentences = dicttagger.tag(pos_tagged_sentences)
#print("analyzing sentiment...")
sentimentscore = sentiment_score(dict_tagged_sentences)
#print(score)
dicttagger1 = DictionaryTagger([ 'Dicts/threatWords.yml','Dicts/Positive.yml', 'Dicts/Negative.yml',
'Dicts/Increasing.yml', 'Dicts/Decreasing.yml', 'Dicts/Inverting.yml'])
splitted_sentences1 = splitter.split(text)
pos_tagged_sentences1 = postagger.pos_tag(splitted_sentences1)
dict_tagged_sentences1 = dicttagger1.tag(pos_tagged_sentences1)
#print("analyzing threat...")
threatscore = threat_score(dict_tagged_sentences1)
average = number_of_threat/wordLength
#print(threatscore)
system = sentimentscore, averages , threatscore, number_of_threat, average
f.write(str(system)+'\n')