def compute_mtld(sentences):
''' Computes the MTLD
:param sentences: sentences
:returns: The MTLD (float)
'''
ll = ' '.join(sentences)
return ld.mtld(ll)
def build_aux_metrics(filename_series, doc_series):
lex_vol = []; ttr = []; mtld = []; vocd = [] # lexical div measures
neg_mean = []; neu_mean = []; pos_mean = []; compound_mean = []
neg_std = []; neu_std = []; pos_std = []; compound_std = []
filename = [] # sentiment measures
for i0 in range(len(doc_series)):
filename0 = filename_series.iloc[i0]; filename0
doc0 = doc_series.iloc[i0]; doc0
doc0_list = nltk.sent_tokenize(doc0); doc0_list
doc0_string = " ".join(doc0_list); doc0_string
n1 = len(doc0_list); n1
if n1 > 1:
vs_list = []
for i1 in range(n1):
sent0 = doc0_list[i1]
vs0 = analyzer.polarity_scores(sent0); vs0
vs_list.append(vs0)
doc0_df = pd.DataFrame(vs_list); doc0_df
mean_list0 = [x for x in doc0_df.mean()]; mean_list0
std_list0 = [x for x in doc0_df.std()]; std_list0
else:
mean_list0 = [float(0) for x in range(4)]; mean_list0
std_list0 = [float(0) for x in range(4)]; std_list0
neg_mean.append(mean_list0[0]); neu_mean.append(mean_list0[1])
pos_mean.append(mean_list0[2]); compound_mean.append(mean_list0[3])
neg_std.append(std_list0[0]); neu_std.append(std_list0[1])
pos_std.append(std_list0[2]); compound_std.append(std_list0[3])
filename.append(filename0)
flt = ld.flemmatize(doc0_string); flt
lex_vol0 = len(flt) # lexical volume measure
ttr0 = ld.ttr(flt) # basic Text-Type Ratio or TTR
mtld0 = ld.mtld(flt) # Measure of Textual Lexical Diversity (MTLD) for lexical variability
vocd0 = ld.hdd(flt) # vocd or Hypergeometric distribution D (HDD), as per McCarthy and Jarvis (2007, 2010)
lex_vol.append(lex_vol0)
ttr.append(ttr0)
mtld.append(mtld0)
vocd.append(vocd0)
if i0%5000 == 0:
print(i0)
# save as df
df1 = pd.DataFrame({'filename':filename,
'senti_neg': neg_mean, 'senti_neu': neu_mean, 'senti_pos': pos_mean, 'senti_compound': compound_mean,
'senti_neg_std': neg_std, 'senti_neu_std': neu_std, 'senti_pos_std': pos_std, 'senti_compound_std': compound_std,
'lex_vol':lex_vol, 'ttr':ttr, 'mtld':mtld, 'vocd':vocd})
return(df1)
def build_aux_metrics1(filename_series, doc_series):
lex_vol = []; mtld = []; # lexical div measures
compound_mean = []; compound_std = [] # sentiment measures
filename = []; #hyp_relev_num =[]
for i0 in range(len(doc_series)):
filename0 = filename_series.iloc[i0]; filename0
doc0 = doc_series.iloc[i0]; doc0
doc0_list = nltk.sent_tokenize(doc0); doc0_list
doc0_string = " ".join(doc0_list); doc0_string
n1 = len(doc0_list); n1
if n1 > 1:
vs_list = []
for i1 in range(n1):
sent0 = doc0_list[i1]
vs0 = analyzer.polarity_scores(sent0); vs0
vs_list.append(vs0)
doc0_df = pd.DataFrame(vs_list); doc0_df
mean_list0 = [x for x in doc0_df.mean()]; mean_list0
std_list0 = [x for x in doc0_df.std()]; std_list0
else:
mean_list0 = [float(0) for x in range(4)]; mean_list0
std_list0 = [float(0) for x in range(4)]; std_list0
compound_mean.append(mean_list0[3]); compound_std.append(std_list0[3])
filename.append(filename0)
flt = ld.flemmatize(str(doc0_string)); flt
lex_vol0 = len(flt) # lexical volume measure
mtld0 = ld.mtld(flt) # Measure of Textual Lexical Diversity (MTLD) for lexical variability
lex_vol.append(lex_vol0)
mtld.append(mtld0)
if i0%5000 == 0:
print(i0)
# save as df
df1 = pd.DataFrame({'filename':filename, 'senti_compound': compound_mean, 'senti_compound_std': compound_std,
'lex_vol':lex_vol, 'mtld':mtld})
return(df1)
def lexdiv(self, text):
self.lexical_diversity = float('{:.2f}'.format(
ld.mtld(ld.flemmatize(text))))
pass
def get_news_features(headline, text):
nlp = es_core_news_md.load()
## headline ##
headline = re.sub(r"http\S+", "", headline)
headline = re.sub(r"http", "", headline)
headline = re.sub(r"@\S+", "", headline)
headline = re.sub("\n", " ", headline)
headline = re.sub(r"(?<!\n)\n(?!\n)", " ", headline)
headline = headline.replace(r"*NUMBER*", "número")
headline = headline.replace(r"*PHONE*", "número")
headline = headline.replace(r"*EMAIL*", "email")
headline = headline.replace(r"*URL*", "url")
headline_lower = headline.lower()
doc_h = nlp(headline_lower)
list_tokens_h = []
list_tags_h = []
for sentence_h in doc_h.sents:
for token in sentence_h:
list_tokens_h.append(token.text)
fdist_h = FreqDist(list_tokens_h)
syllables_h = get_nsyllables(headline)
words_h = len(list_tokens_h)
# headline complexity features
avg_word_size_h = round(
sum(len(word) for word in list_tokens_h) / words_h, 2)
avg_syllables_word_h = round(syllables_h / words_h, 2)
unique_words_h = round((len(fdist_h.hapaxes()) / words_h) * 100, 2)
mltd_h = round(ld.mtld(list_tokens_h), 2)
ttr_h = round(ld.ttr(list_tokens_h) * 100, 2)
## text content##
text = re.sub(r"http\S+", "", text)
text = re.sub(r"http", "", text)
text = re.sub("\n", " ", text)
text = text.replace(r"*NUMBER*", "número")
text = text.replace(r"*PHONE*", "número")
text = text.replace(r"*EMAIL*", "email")
text = text.replace(r"*URL*", "url")
# to later calculate upper case letters ratio
alph = list(filter(str.isalpha, text))
text_lower = text.lower()
doc = nlp(text_lower)
list_tokens = []
list_pos = []
list_tag = []
list_entities = []
sents = 0
for entity in doc.ents:
list_entities.append(entity.label_)
for sentence in doc.sents:
sents += 1
for token in sentence:
list_tokens.append(token.text)
list_pos.append(token.pos_)
list_tag.append(token.tag_)
# Calculate entities, pos, tag, freq, syllables, words and quotes
entities = len(list_entities)
n_pos = nltk.Counter(list_pos)
n_tag = nltk.Counter(list_tag)
fdist = FreqDist(list_tokens)
syllables = get_nsyllables(text)
words = len(list_tokens)
quotes = n_tag['PUNCT__PunctType=Quot']
# complexity features
avg_word_sentence = round(words / sents, 2)
avg_word_size = round(sum(len(word) for word in list_tokens) / words, 2)
avg_syllables_word = round(syllables / words, 2)
unique_words = round((len(fdist.hapaxes()) / words) * 100, 2)
ttr = round(ld.ttr(list_tokens) * 100, 2)
# readability spanish test
huerta_score = round(
206.84 - (60 * avg_syllables_word) - (1.02 * avg_word_sentence), 2)
szigriszt_score = round(
206.835 - ((62.3 * syllables) / words) - (words / sents), 2)
# stylometric features
mltd = round(ld.mtld(list_tokens), 2)
upper_case_ratio = round(sum(map(str.isupper, alph)) / len(alph) * 100, 2)
entity_ratio = round((entities / words) * 100, 2)
quotes_ratio = round((quotes / words) * 100, 2)
propn_ratio = round((n_pos['PROPN'] / words) * 100, 2)
noun_ratio = round((n_pos['NOUN'] / words) * 100, 2)
pron_ratio = round((n_pos['PRON'] / words) * 100, 2)
adp_ratio = round((n_pos['ADP'] / words) * 100, 2)
det_ratio = round((n_pos['DET'] / words) * 100, 2)
punct_ratio = round((n_pos['PUNCT'] / words) * 100, 2)
verb_ratio = round((n_pos['VERB'] / words) * 100, 2)
adv_ratio = round((n_pos['ADV'] / words) * 100, 2)
sym_ratio = round((n_tag['SYM'] / words) * 100, 2)
# create df_features
df_features = pd.DataFrame({
'text': text_lower,
'headline': headline_lower,
'words_h': words_h,
'word_size_h': [avg_word_size_h],
'avg_syllables_word_h': [avg_syllables_word_h],
'unique_words_h': [unique_words_h],
'ttr_h': ttr_h,
'mltd_h': [mltd_h],
'sents': sents,
'words': words,
'avg_words_sent': [avg_word_sentence],
'avg_word_size': [avg_word_size],
'avg_syllables_word': avg_syllables_word,
'unique_words': [unique_words],
'ttr': [ttr],
'huerta_score': [huerta_score],
'szigriszt_score': [szigriszt_score],
'mltd': [mltd],
'upper_case_ratio': [upper_case_ratio],
'entity_ratio': [entity_ratio],
'quotes': quotes,
'quotes_ratio': [quotes_ratio],
'propn_ratio': [propn_ratio],
'noun_ratio': [noun_ratio],
'pron_ratio': [pron_ratio],
'adp_ratio': [adp_ratio],
'det_ratio': [det_ratio],
'punct_ratio': [punct_ratio],
'verb_ratio': [verb_ratio],
'adv_ratio': [adv_ratio],
'sym_ratio': [sym_ratio]
})
return df_features
def lexical_mtld(tokens):
return lex_div.mtld(tokens)
def mtld_ld(text):
tokens = BasicMetrics.trueTokens(text)
return ld.mtld(tokens)
