class ValidatorClass:
"""Used to validate text and location attributes of a tweet """
def __init__(self, path_to_pickle_files):
self.banned_word_list = ['rt ', 'https', 'jab']
self.text_classifier = TweetClassifier(path_to_pickle_files)
def validate_location(self, location):
"""
Checks if location id None or "None"
:param location: string
:return: True/False: bool
"""
if location is None:
return False
if location == 'None':
return False
return True
def validate_text_from_tweet(self, text_from_tweet):
"""
Checks if tweet is an empty string
Checks if tweet contains banned words
If above not true gets sentiment of text
:param text_from_tweet:
:return: True/False: bool
"""
if text_from_tweet == '':
return False
for banned_word in self.banned_word_list:
if banned_word in text_from_tweet:
return False
validText = self.text_classifier.sentiment(text_from_tweet)
return validText
print("LogisticRegression_classifier accuracy percent:",
(nltk.classify.accuracy(LogisticRegression_classifier, testing_set)) *
100)
save_to_pickle_file(path_to_pickle_files,
"LogisticRegression_classifier.pickle",
LogisticRegression_classifier)
SGDClassifier_classifier = SklearnClassifier(SGDClassifier())
SGDClassifier_classifier.train(training_set)
print("SGDClassifier_classifier accuracy percent:",
(nltk.classify.accuracy(SGDClassifier_classifier, testing_set)) * 100)
save_to_pickle_file(path_to_pickle_files, "SGDClassifier_classifier.pickle",
SGDClassifier_classifier)
LinearSVC_classifier = SklearnClassifier(LinearSVC())
LinearSVC_classifier.train(training_set)
print("LinearSVC_classifier accuracy percent:",
(nltk.classify.accuracy(LinearSVC_classifier, testing_set)) * 100)
save_to_pickle_file(path_to_pickle_files, "LinearSVC_classifier.pickle",
LinearSVC_classifier)
NuSVC_classifier = SklearnClassifier(NuSVC())
NuSVC_classifier.train(training_set)
print("NuSVC_classifier accuracy percent:",
(nltk.classify.accuracy(NuSVC_classifier, testing_set)) * 100)
save_to_pickle_file(path_to_pickle_files, "NuSVC_classifier.pickle",
NuSVC_classifier)
voted_classifier = TweetClassifier("pickle_files/")
print("voted_classifier accuracy percent:",
(nltk.classify.accuracy(voted_classifier, testing_set)) * 100)
def __init__(self, path_to_pickle_files):
self.banned_word_list = ['rt ', 'https', 'jab']
self.text_classifier = TweetClassifier(path_to_pickle_files)
