def get_ngram_feature_vector(post, n, ngram_word_corpus: dict):
"""
Function that creates the n-gram feature vector of a post
:arg post: The post the we want to extract the n-gram features from
:arg n: The n in n-gram
:arg ngram_word_corpus: The entire feature vector initialized with zeroes
:return: The final n-gram feature vector of the specified post
"""
# Make a copy of the initialized feature vector to avoid changing it by reference
ngram_feature_vector = ngram_word_corpus.copy()
# Find the post's n-grams
grams = ngrams((utils.article(post)).split(), n)
# For each n-gram in the post
for g in grams:
k = re.sub(
r'[^a-zA-Z0-9 ]+', '',
(" ".join(g)
)).lower() # Remove special characters and make it lowercase
# If it exists in our initialized feature vector add 1
if k in ngram_feature_vector.keys():
ngram_feature_vector[k] += 1
# Return the post's feature vector
return ngram_feature_vector
def get_no_of_characters_features(post):
"""
Calculates the "Number of characters" features. 4 features
are calculated in total
:return: a list that contains the features
"""
f1 = utils.len_characters(utils.title(post))
f2 = utils.len_characters(utils.article(post))
f3 = utils.len_characters(utils.description(post))
f4 = utils.len_characters(utils.keywords(post))
return [f1, f2, f3, f4]
def get_slang_words_feature(post):
"""
Checks whether the post's text and article's title contain
slang words
:param post: the current post
:return: a list of 0s, 1s indicating whether the fields contain
slang words
"""
post_text = utils.title(post)
article_title = utils.article(post)
found_in_post_text = has_slang_words(post_text)
found_in_article_title = has_slang_words(article_title)
return [found_in_post_text, found_in_article_title]
def get_common_clickbait_phrases_feature(post):
"""
Checks whether the post's text and article's title contain
common words/phrases
:param post: the current post
:return: a list of 0s, 1s indicating whether the fields contain
common words/phrases
"""
post_text = utils.title(post)
article_title = utils.article(post)
found_in_post_text = has_common_phrases(post_text)
found_in_article_title = has_common_phrases(article_title)
return [found_in_post_text, found_in_article_title]
def get_ngram_corpus(n, lower_t, upper_t):
"""
Function that initializes the dataset's n-gram feature vectors for given n
:arg n: The n in n-gram
:arg lower_t: The lower threshold that removes n-grams with lower counts than the threshold in the dataset
:arg upper_t: The upper threshold that removes n-grams with higher counts than the threshold in the dataset
:return: The final n-gram feature vectors after the upper and lower pruning initialized to zero
"""
counts = {} # The dictionary that hold the n-gram occurrences
# For every post in the dataset
with open('dataset/instances.jsonl', 'rb') as f:
for post in json_lines.reader(f):
grams = ngrams((utils.article(post)).split(),
n) # Get the post's n-grams
# For every n-gram
for g in grams:
k = re.sub(r'[^a-zA-Z0-9 ]+', '',
(" ".join(g))) # Remove special characters
# If the n-gram is NNP don't take it into account
if utils.POS_counts(k)['NNP'] == 0:
k = k.lower() # make i lowercase
# Increment the count dictionary
if k in counts.keys():
counts[k] += 1
else:
counts[k] = 1
# Create the final feature vector taking ito account the counts dictionary and the upper and lower thresholds
ng = {k: 0 for k, v in counts.items() if v > lower_t and not v >= upper_t}
# Write the results into a csv in order to plot the n-gram distributions afterwards
pd.DataFrame(counts.items(), columns=['gram', 'count']).to_csv(
"dataset/" + str(n) + "-gram_frequencies.csv", index=False)
# Return the final feature vector with 0 values
return ng
def get_hyperbolic_words_feature(connection, post):
"""
Checks if there are any hyperbolic words in the provided
post's texts and article's title. NOTE! This needs the NLP Stanford
server to be up and running.
:param connection: the connection to the stanford local server
:param post: the current post
:return: a list with 1s or 0s based on whether the post text/article title
contains at least one hyperbolic word or not
"""
post_text = utils.title(post)
article_title = utils.article(post)
found_in_post_text = has_hyperbolic_words(connection, post_text)
found_in_article_title = has_hyperbolic_words(connection, article_title)
return [found_in_post_text, found_in_article_title]
def get_no_of_characters_ratio_features(post):
"""
Calculates the "Number of characters ratio". 6 features
are calculated in total
:return: a list that contains the features
"""
post_title_len = utils.len_characters(utils.title(post))
article_title_len = utils.len_characters(utils.article(post))
article_desc_len = utils.len_characters(utils.description(post))
article_keywords_len = utils.len_characters(utils.keywords(post))
lst = [
post_title_len, article_title_len, article_desc_len,
article_keywords_len
]
features_lst = get_ratio_features_list(lst)
return features_lst
def get_diff_between_no_of_characters_features(post):
"""
Calculates the "Difference between number of chars" features.
6 features are calculated in total
:return: a list that contains the features
"""
post_title_len = utils.len_characters(utils.title(post))
article_title_len = utils.len_characters(utils.article(post))
article_desc_len = utils.len_characters(utils.description(post))
article_keywords_len = utils.len_characters(utils.keywords(post))
lst = [
post_title_len, article_title_len, article_desc_len,
article_keywords_len
]
features_lst = get_difference_features_list(lst)
return features_lst
def get_sentiment_polarity_feature(post):
"""
Calculates the compound score of the post's text and the
article's title
:param post: the current post
:return: a list with the compound scores
"""
post_text = utils.title(post)
article_title = utils.article(post)
# If list extract the element into a string
if isinstance(post_text, list):
post_text = post_text[0]
if isinstance(article_title, list):
article_title = article_title[0]
scores_post_text = analyser.polarity_scores(post_text)
scores_article_title = analyser.polarity_scores(article_title)
return [scores_post_text["compound"], scores_article_title["compound"]]
def main():
# Creating label dictionary
labels = utils.get_label_dict()
with open('dataset/instances.jsonl', 'rb') as f:
headers = False
count = 0 # elements processed
for post in json_lines.reader(f):
count += 1
print('Sample', count)
# Reading post/article elements
post_id = utils.post_id(post)
post_title = utils.title(post)
article_title = utils.article(post)
# Extracting sample label
post_label = labels[post_id]
# Presence of image in a post
has_image = imf.image_presence(post)
# Number of characters
len_chars_post_title, len_chars_article_title, len_chars_article_desc, len_chars_article_keywords = \
laf.get_no_of_characters_features(post)
# Difference between number of characters
diff_chars_post_title_article_title, diff_chars_post_title_article_desc, diff_chars_post_title_article_keywords, \
diff_chars_article_title_article_desc, diff_chars_article_title_article_keywords, diff_chars_article_desc_article_keywords = \
laf.get_diff_between_no_of_characters_features(post)
# Number of characters ratio
ratio_chars_post_title_article_title, ratio_chars_post_title_article_desc, ratio_chars_post_title_article_keywords, \
ratio_chars_article_title_article_desc, ratio_chars_article_title_article_keywords, ratio_chars_article_desc_article_keywords = \
laf.get_no_of_characters_ratio_features(post)
# Number of Words
len_words_post_title, len_words_article_title, len_words_article_desc, len_words_article_keywords = \
laf.get_no_of_characters_features(post)
# Difference between number of words
diff_words_post_title_article_title, diff_words_post_title_article_desc, diff_words_post_title_article_keywords, \
diff_words_article_title_article_desc, diff_words_article_title_article_keywords, diff_words_article_desc_article_keywords = \
laf.get_diff_between_no_of_words_features(post)
# Number of words ratio
ratio_words_post_title_article_title, ratio_words_post_title_article_desc, ratio_words_post_title_article_keywords, \
ratio_words_article_title_article_desc, ratio_words_article_title_article_keywords, ratio_words_article_desc_article_keywords = \
laf.get_no_of_words_ratio_features(post)
# Post creation hour
post_creation_hour = adf.get_post_creation_hour(post)
# Number of sings
post_title_no_signs = adf.get_no_signs(post_title)
# Number of hashtags
post_title_no_hashtags = adf.get_no_hashtags(post_title)
# Number of exclamations
post_title_no_exclamations = adf.get_no_exclamations(post_title)
article_title_no_exclamations = adf.get_no_exclamations(
article_title)
# Number of question marks
post_title_no_questionmarks = adf.get_no_question_marks(post_title)
article_title_no_questionmarks = adf.get_no_question_marks(
article_title)
# Number of abbreviations
post_title_no_abbreviations = adf.get_no_abbreviations(post_title)
article_title_no_abbreviations = adf.get_no_abbreviations(
article_title)
# Number of ellipses
post_title_no_ellipses = adf.get_no_ellipses(post_title)
article_title_no_ellipses = adf.get_no_ellipses(article_title)
# Number of dots
post_title_no_dots = adf.get_no_dots(post_title)
article_title_no_dots = adf.get_no_dots(article_title)
# Begins with interrogative
post_title_begins_with_interrogative = adf.get_begins_with_interrogative(
post_title)
article_title_begins_with_interrogative = adf.get_begins_with_interrogative(
article_title)
# Begins with number
post_title_begins_with_number = adf.get_begins_with_number(
post_title)
article_title_begins_with_number = adf.get_begins_with_number(
article_title)
# Contains determiners and possessives
post_title_determiners, post_title_possessives = laf.get_det_poses(
post_title)
article_title_determiners, article_title_possessives = laf.get_det_poses(
article_title)
# Contains hyperbolic words
try:
nlp = StanfordCoreNLP('http://*****:*****@,Post_Title_No_#,' \
'Post_Title_No_Exclam,Article_Title_No_Exclam,Post_Title_No_Question,Article_Title_No_Question,Post_Title_No_Abbrev,' \
'Article_Title_No_Abbrev,Post_Title_No_Ellipses,Article_Title_No_Ellipses,Post_Title_No_Dots,Article_Title_No_Dots'
for key, value in counts_post_title_POS.items():
feature_headers += ',Post_Title_' + key
for key, value in counts_article_title_POS.items():
feature_headers += ',Article_Title_' + key
feature_headers += ',Post_Title_NNPV,Post_Title_NNPT'
feature_headers += ',Article_Title_NNPV,Article_Title_NNPT'
for key, value in unigrams.items():
feature_headers += ',' + key
for key, value in bigrams.items():
feature_headers += ',' + key
for key, value in trigrams.items():
feature_headers += ',' + key
# Writing file headlines
with open('dataset/features.csv',
encoding='utf8',
mode='w',
newline='') as features_file:
features_writer = csv.writer(features_file,
delimiter=',',
quotechar='"',
quoting=csv.QUOTE_MINIMAL)
features_writer.writerow([feature_headers])
headers = True
with open('dataset/features.csv',
encoding='utf8',
mode='a',
newline='') as features_file:
features_writer = csv.writer(features_file,
delimiter=',',
quotechar='"',
quoting=csv.QUOTE_MINIMAL)
features_writer.writerow([feature_output])
