def lex_readability(self, text, mode='fre'):
if mode == 'all':
fre_score = textstat.flesch_reading_ease(text)
fog_index = textstat.gunning_fog(text)
fkg_index = textstat.flesch_kincaid_grade(text)
dcr_score = textstat.dale_chall_readability_score(text)
text_standard = textstat.text_standard(text, float_output=True)
return fre_score, fog_index, fkg_index, dcr_score, text_standard
if mode == 'fre':
fre_score = textstat.flesch_reading_ease(text)
return fre_score
if mode == 'fog':
fog_index = textstat.gunning_fog(text)
return fog_index
if mode == 'fkg':
fkg_index = textstat.flesch_kincaid_grade(text)
return fkg_index
if mode == 'dcr':
dcr_score = textstat.dale_chall_readability_score(text)
return dcr_score
if mode == 'text_std':
text_standard = textstat.text_standard(text, float_output=True)
return text_standard
def test_gunning_fog():
score = textstat.gunning_fog(long_test)
assert score == 11.26
# FOG-PL
score_pl = textstat.gunning_fog(long_test, lang='pl_PL')
assert score_pl == 10.40
def test_gunning_fog():
textstat.set_lang("en_US")
score = textstat.gunning_fog(long_test)
assert score == 11.26
# FOG-PL
textstat.set_lang("pl_PL")
score_pl = textstat.gunning_fog(long_test)
assert score_pl == 10.40
def read_metrics(text_clean):
table = {}
#table['flesch'] = textstat.flesch_reading_ease(text_clean)
#table['flesch_kincaid'] = textstat.flesch_kincaid_grade(text_clean)
table['fog'] = textstat.gunning_fog(text_clean)
table['smog'] = textstat.smog_index(text_clean)
table['ari'] = textstat.automated_readability_index(text_clean)
table['coleman_liau'] = textstat.coleman_liau_index(text_clean)
r_read_mets = quanteda.textstat_readability(text_clean, measure='all')
table['ari_r'] = float(r_read_mets[1].r_repr())
table['rix_r'] = float(r_read_mets[35].r_repr())
table['Coleman_Liau_Grade_R'] = float(r_read_mets[9].r_repr())
table['Coleman_Liau_Short_R'] = float(r_read_mets[10].r_repr())
table['Danielson_Bryan_R'] = float(r_read_mets[14].r_repr())
table['Dickes_Steiwer_R'] = float(r_read_mets[16].r_repr())
table['ELF_R'] = float(r_read_mets[18].r_repr())
table['Farr_Jenkins_Paterson_R'] = float(r_read_mets[19].r_repr())
table['flesch_R'] = float(r_read_mets[20].r_repr())
table['flesh_kincaid_R'] = float(r_read_mets[22].r_repr())
table['FORCAST_R'] = float(r_read_mets[26].r_repr())
table['Fucks_R'] = float(r_read_mets[28].r_repr())
table['FOG_R'] = float(r_read_mets[23].r_repr())
table['Linsear_Write_R'] = float(r_read_mets[29].r_repr())
table['nWS_R'] = float(r_read_mets[31].r_repr())
table['SMOG_R'] = float(r_read_mets[37].r_repr())
table['Strain_R'] = float(r_read_mets[43].r_repr())
table['Wheeler_Smith_R'] = float(r_read_mets[46].r_repr())
return table
def get_readibility(text, metric="flesch_kincaid_grade"):
"""
Return a score which reveals a piece of text's readability level.
Reference: https://chartbeat-labs.github.io/textacy/getting_started/quickstart.html
https://en.wikipedia.org/wiki/Flesch%E2%80%93Kincaid_readability_tests
"""
if metric == "flesch_kincaid_grade":
result = textstat.flesch_kincaid_grade(text)
elif metric == "flesch_reading_ease":
result = textstat.flesch_reading_ease(text)
elif metric == "smog_index":
result = textstat.smog_index(text)
elif metric == "coleman_liau_index":
result = textstat.coleman_liau_index(text)
elif metric == "automated_readability_index":
result = textstat.automated_readability_index(text)
elif metric == "dale_chall_readability_score":
result = textstat.dale_chall_readability_score(text)
elif metric == "difficult_words":
result = textstat.difficult_words(text)
elif metric == "linsear_write_formula":
result = textstat.linsear_write_formula(text)
elif metric == "gunning_fog":
result = textstat.gunning_fog(text)
elif metric == "text_standard":
result = textstat.text_standard(text)
else:
print("ERROR: Please select correct metric!")
result = None
return result
def getReadabilityMetrics(test_data):
'''
for a given article IN TEXT FORMAT, returns its readability metrics
Uses textstat library, please install it
'''
metric = {
"flesch_reading_ease":
textstat.flesch_reading_ease(test_data),
"smog_index":
textstat.smog_index(test_data),
"flesch_kincaid_grade":
textstat.flesch_kincaid_grade(test_data),
"coleman_liau_index":
textstat.coleman_liau_index(test_data),
"automated_readability_index":
textstat.automated_readability_index(test_data),
"dale_chall_readability_score":
textstat.dale_chall_readability_score(test_data),
"difficult_words":
textstat.difficult_words(test_data),
"linsear_write_formula":
textstat.linsear_write_formula(test_data),
"gunning_fog":
textstat.gunning_fog(test_data),
"text_standard":
textstat.text_standard(test_data)
}
return metric
def get_readability_score(text, metric="flesch"):
global tknzr, DIFFICULT
text = text.replace("’", "'")
# https://pypi.org/project/textstat/
if metric == "flesch":
return textstat.flesch_reading_ease(text)
elif metric == "smog":
return textstat.smog_index(text)
elif metric == "coleman_liau_index":
return textstat.coleman_liau_index(text)
elif metric == "automated_readability_index":
return textstat.automated_readability_index(text)
elif metric == "dale_chall_readability_score":
return textstat.dale_chall_readability_score(text)
elif metric == "difficult_words":
nb_difficult = 0
nb_easy = 0
for w in set(tknzr.tokenize(text.lower())):
if w not in EASY_WORDS and len(w) >= 6:
nb_difficult += 1
else:
nb_easy += 1
return 100 * nb_difficult / (nb_difficult + nb_easy)
#return textstat.difficult_words(text)#/len(text.split())
elif metric == "linsear_write_formula":
return textstat.linsear_write_formula(text)
elif metric == "gunning_fog":
return textstat.gunning_fog(text)
elif metric == "avg_word_length":
words = tknzr.tokenize(text)
words = [w for w in words if w not in misc_utils.PUNCT]
if len(words) == 0: return 0
return np.average([len(w) for w in words])
def readability_measures(self, as_dict=False):
"""
Return the BOFIR score as well as other classic readability formulas for the paragraph.
Parameters
----------
as_dict : boolean
Defines if output is a dataframe or dict
Returns
-------
d: DataFrame
DataFrame with the BOFIR score and additional readability measures
"""
flesch = self.flesch
smog = textstat.smog_index(self.paragraph)
dale_chall = textstat.dale_chall_readability_score(self.paragraph)
fog = textstat.gunning_fog(self.paragraph)
bofir_5cat = self.bofir(cat5=True)
bofir_3cat = self.bofir(cat5=False)
d = {
'bofir_5cat': bofir_5cat,
'bofir_3cat': bofir_3cat,
'fog': fog,
'dale_chall': dale_chall,
'smog': smog,
'flesch': flesch
}
if as_dict:
return d
else:
return pd.DataFrame(d, index=['readability_score'])
def get_stats(text):
fre = textstat.flesch_reading_ease(text)
smog = textstat.smog_index(text)
fkg = textstat.flesch_kincaid_grade(text)
cli = textstat.coleman_liau_index(text)
ari = textstat.automated_readability_index(text)
dcr = textstat.dale_chall_readability_score(text)
diff_words = textstat.difficult_words(text)
lwf = textstat.linsear_write_formula(text)
gunn_fog = textstat.gunning_fog(text)
consolidated_score = textstat.text_standard(text)
doc_length = len(text) # think about excluding spaces?
quote_count = text.count('"')
stats = {
"flesch_reading_ease": fre,
"smog_index": smog,
"flesch_kincaid_grade": fkg,
"coleman_liau_index": cli,
"automated_readability_index": ari,
"dale_chall_readability_score": dcr,
"difficult_words": diff_words,
"linsear_write_formula": lwf,
"gunning_fog": gunn_fog,
"consolidated_score": consolidated_score,
"doc_length": doc_length,
"quote_count": quote_count
}
return stats
def seven_test(processed_essay):
"""
score which is assigned to every script in on the basis of some predifened fomulas
These scores are known as readability score.
flesch_score,gunning_index,kincaid_grade,liau_index,automated_readability_index,dale_readability_score,difficult_word,linsear_write
:param processed_essay:
:return:flesch_score,gunning_index,kincaid_grade,liau_index,automated_readability_index,dale_readability_score,difficult_word,linsear_write
"""
flesch_score = ["FS"]
gunning_index = ["GI"]
kincaid_grade = ["KG"]
liau_index = ["LI"]
automated_readability_index = ["ARI"]
dale_readability_score = ["DLS"]
difficult_word = ["DW"]
linsear_write = ["LW"]
for v in processed_essay:
flesch_score.append(textstat.flesch_reading_ease(str(v)))
gunning_index.append(textstat.gunning_fog(str(v)))
kincaid_grade.append(textstat.flesch_kincaid_grade(str(v)))
liau_index.append(textstat.coleman_liau_index(str(v)))
automated_readability_index.append(textstat.automated_readability_index(str(v)))
dale_readability_score.append(textstat.dale_chall_readability_score(str(v)))
difficult_word.append(textstat.difficult_words(str(v)))
linsear_write.append(textstat.linsear_write_formula(str(v)))
return flesch_score,gunning_index,kincaid_grade,liau_index,automated_readability_index,dale_readability_score,difficult_word,linsear_write
def textstat_stats(text):
doc_length = len(text.split())
flesch_ease = ts.flesch_reading_ease(text) #Flesch Reading Ease Score
flesch_grade = ts.flesch_kincaid_grade(text) #Flesch-Kincaid Grade Level
gfog = ts.gunning_fog(text) # FOG index, also indicates grade level
# smog = ts.smog_index(text) # SMOG index, also indicates grade level, only useful on 30+ sentences
auto_readability = ts.automated_readability_index(text) #approximates the grade level needed to comprehend the text.
cl_index = ts.coleman_liau_index(text) #grade level of the text using the Coleman-Liau Formula.
lw_formula = ts.linsear_write_formula(text) #grade level using the Linsear Write Formula.
dcr_score = ts.dale_chall_readability_score(text) #uses a lookup table of the most commonly used 3000 English words
# text_standard = ts.text_standard(text, float_output=False) # summary of all the grade level functions
syll_count = ts.syllable_count(text, lang='en_US')
syll_count_scaled = syll_count / doc_length
lex_count = ts.lexicon_count(text, removepunct=True)
lex_count_scaled = lex_count / doc_length
idx = ['flesch_ease', 'flesch_grade','gfog',
'auto_readability','cl_index','lw_formula',
'dcr_score',
# 'text_standard',
'syll_count', 'lex_count']
return pd.Series([flesch_ease, flesch_grade, gfog,
auto_readability, cl_index, lw_formula,
dcr_score,
# text_standard,
syll_count_scaled, lex_count_scaled], index = idx)
def analyze():
print(request)
str_to_read = request.data.decode("utf-8").strip()
report = {
"flesch-reading-ease":
textstat.flesch_reading_ease(str_to_read),
"smog-index":
textstat.smog_index(str_to_read),
"flesch-kincaid-grade":
textstat.flesch_kincaid_grade(str_to_read),
"coleman-liau-index":
textstat.coleman_liau_index(str_to_read),
"automated-readability-index":
textstat.automated_readability_index(str_to_read),
"dale-chall-readability-score":
textstat.dale_chall_readability_score(str_to_read),
"difficult-words":
textstat.difficult_words(str_to_read),
"linsear-write-formula":
textstat.linsear_write_formula(str_to_read),
"gunning-fog":
textstat.gunning_fog(str_to_read),
"text-standard":
textstat.text_standard(str_to_read)
}
return decorate_response(jsonify(report))
def readability(queries):
scores = pd.DataFrame(columns=[
'Flesch', 'Smog', 'Flesch grade', 'Coleman', 'Automated', 'Dale',
'Difficult', 'Linsear', 'Gunning', 'Text Standard'
])
scores = {
'Flesch': [],
'Smog': [],
'Flesch grade': [],
'Coleman': [],
'Automated': [],
'Dale': [],
'Difficult': [],
'Linsear': [],
'Gunning': [],
'Text Standard': []
}
for line in queries:
# results = readability.getmeasures(line, lang='en')
# frescores.append(results['readability grades']['FleschReadingEase'])
# line = 'yao family wines . yao family wines is a napa valley producer founded in 2011 by yao ming , the chinese-born , five-time nba all star . now retired from the houston rockets , yao ming is the majority owner in yao family wines , which has entered the wine market with a luxury cabernet sauvignon sourced from napa valley vineyards .'
scores['Flesch'].append(textstat.flesch_reading_ease(line))
scores['Smog'].append(textstat.smog_index(line))
scores['Flesch grade'].append(textstat.flesch_kincaid_grade(line))
scores['Coleman'].append(textstat.coleman_liau_index(line))
scores['Automated'].append(textstat.automated_readability_index(line))
scores['Dale'].append(textstat.dale_chall_readability_score(line))
scores['Difficult'].append(textstat.difficult_words(line))
scores['Linsear'].append(textstat.linsear_write_formula(line))
scores['Gunning'].append(textstat.gunning_fog(line))
scores['Text Standard'].append(
textstat.text_standard(line, float_output=True))
return scores
def _teach_chatbot(cls, response, statement):
if (len(response) not in cls.teach_if_len_in
or textstat.gunning_fog(response) > cls.maximum_gunning_fog):
return
print(f'Teaching bot to answer to "{statement}" with "{response}"')
CLIENT.chatbot.learn_response(Statement(response),
Statement(statement))
def score(text):
a = textstat.flesch_reading_ease(text)
b = textstat.flesch_kincaid_grade(text)
c = textstat.gunning_fog(text)
d = textstat.smog_index(text)
e = textstat.coleman_liau_index(text)
f = textstat.automated_readability_index(text)
return a, b, c, d, e, f
def do_datas():
# logging.info('do_datas')
########### Save text statistics
##### 1. nw 2. nvocab 3. nsyllable 4.nsentence 5. tone 6. readability
## 1. nw
nw.append(len(words))
## 2. nvocab
nvocab.append(len(vocab))
## 3. syllable
n = textstat.syllable_count(contents)
nsyllable.append(n)
## 4. sentence
n = textstat.sentence_count(contents)
nsentence.append(n)
## 5. tone
### LM dictionary
n_neg_lm.append(count_occurrence(words, lm_neg))
n_pos_lm.append(count_occurrence(words, lm_pos))
n_uctt_lm.append(count_occurrence(words, lm_uctt))
n_lit_lm.append(count_occurrence(words, lm_lit))
n_cstr_lm.append(count_occurrence(words, lm_cstr))
n_modal1_lm.append(count_occurrence(words, lm_modal1))
n_modal2_lm.append(count_occurrence(words, lm_modal2))
n_modal3_lm.append(count_occurrence(words, lm_modal3))
n_negation_lm.append(count_negation(words, lm_pos, gt_negation))
### General Inquirer dictionary
n_neg_gi.append(count_occurrence(words, gi_neg))
n_pos_gi.append(count_occurrence(words, gi_pos))
n_negation_gi.append(count_negation(words, gi_pos, gt_negation))
### Henry dictionary
n_neg_hr.append(count_occurrence(words, hr_neg))
n_pos_hr.append(count_occurrence(words, hr_pos))
n_negation_hr.append(count_negation(words, gi_pos, gt_negation))
## 4. readability
fre_i = textstat.flesch_reading_ease(contents)
if fre_i > 100:
fre_i = 100
if fre_i < 0:
fre_i = float('NaN')
fre.append(fre_i)
fkg_i = textstat.flesch_kincaid_grade(contents)
if fkg_i < 0:
fkg_i = float('NaN')
fkg.append(fkg_i)
# RIX
cl_i = textstat.coleman_liau_index(contents)
if cl_i < 0:
cl_i = float('NaN')
cl.append(cl_i)
f = textstat.gunning_fog(contents)
fog.append(f)
f = textstat.automated_readability_index(contents)
ari.append(f)
f = textstat.smog_index(contents)
smog.append(f)
def doc_calc(self, article):
"""Helper code to compute average word length of a name"""
flesch_ease = textstat.flesch_reading_ease(article)
flesch_grade = textstat.flesch_kincaid_grade(article)
gunning = textstat.gunning_fog(article)
profanity = predict_prob([article])[0]
polarity = TextBlob(article).sentiment.polarity
return pd.Series(
[flesch_ease, flesch_grade, gunning, profanity, polarity])
def metrics(sentence):
fk = round(flesch_kincaid_grade(sentence), 3)
gf = round(gunning_fog(sentence), 3)
dc = round(dale_chall_readability_score(sentence), 3)
fk_label = grade_label(round(fk))
gf_label = grade_label(round(gf))
dc_label = grade_label(dale_chall_norm(round(dc)))
return (fk, gf, dc, fk_label, gf_label, dc_label)
def compute_readability_stats(text):
"""
Compute reading statistics of the given text
Reference: https://github.com/shivam5992/textstat
Parameters
==========
text: str, input section or abstract text
"""
try:
readability_dict = {
'flesch_reading_ease':
textstat.flesch_reading_ease(text),
'smog':
textstat.smog_index(text),
'flesch_kincaid_grade':
textstat.flesch_kincaid_grade(text),
'coleman_liau_index':
textstat.coleman_liau_index(text),
'automated_readability_index':
textstat.automated_readability_index(text),
'dale_chall':
textstat.dale_chall_readability_score(text),
'difficult_words':
textstat.difficult_words(text),
'linsear_write':
textstat.linsear_write_formula(text),
'gunning_fog':
textstat.gunning_fog(text),
'text_standard':
textstat.text_standard(text),
'n_syllable':
textstat.syllable_count(text),
'avg_letter_per_word':
textstat.avg_letter_per_word(text),
'avg_sentence_length':
textstat.avg_sentence_length(text)
}
except:
readability_dict = {
'flesch_reading_ease': None,
'smog': None,
'flesch_kincaid_grade': None,
'coleman_liau_index': None,
'automated_readability_index': None,
'dale_chall': None,
'difficult_words': None,
'linsear_write': None,
'gunning_fog': None,
'text_standard': None,
'n_syllable': None,
'avg_letter_per_word': None,
'avg_sentence_length': None
}
return readability_dict
def score(self, strText):
self.automated_readability_index = textstat.automated_readability_index(
strText)
self.str_automated_readability_index = self.grade(
self.automated_readability_index)
self.coleman_liau_index = textstat.coleman_liau_index(strText)
self.str_coleman_liau_index = self.grade(self.coleman_liau_index)
self.dale_chall_readability_score = textstat.dale_chall_readability_score(
strText)
if self.dale_chall_readability_score >= 9.0:
self.str_dale_chall_readability_score = ' | ' + '13th to 15th grade (college)'
elif self.dale_chall_readability_score >= 8.0:
self.str_dale_chall_readability_score = ' | ' + '11th to 12th grade'
elif self.dale_chall_readability_score >= 7.0:
self.str_dale_chall_readability_score = ' | ' + '9th to 10th grade'
elif self.dale_chall_readability_score >= 6.0:
self.str_dale_chall_readability_score = ' | ' + '7th to 8th grade'
elif self.dale_chall_readability_score >= 5.0:
self.str_dale_chall_readability_score = ' | ' + '5th to 6th grade'
else:
self.str_dale_chall_readability_score = ' | ' + '4th grade or lower'
self.difficult_words = textstat.difficult_words(strText)
self.flesch_kincaid_grade = textstat.flesch_kincaid_grade(strText)
self.str_flesch_kincaid_grade = self.grade(self.flesch_kincaid_grade)
self.flesch_reading_ease = textstat.flesch_reading_ease(strText)
if self.flesch_reading_ease >= 90:
self.str_flesch_reading_ease = ' | ' + 'Very Easy'
elif self.flesch_reading_ease >= 80:
self.str_flesch_reading_ease = ' | ' + 'Easy'
elif self.flesch_reading_ease >= 70:
self.str_flesch_reading_ease = ' | ' + 'Fairly Easy'
elif self.flesch_reading_ease >= 60:
self.str_flesch_reading_ease = ' | ' + 'Standard'
elif self.flesch_reading_ease >= 50:
self.str_flesch_reading_ease = ' | ' + 'Fairly Difficult'
elif self.flesch_reading_ease >= 30:
self.str_flesch_reading_ease = ' | ' + 'Difficult'
else:
self.str_flesch_reading_ease = ' | ' + 'Very Confusing'
self.gunning_fog = textstat.gunning_fog(strText)
self.str_gunning_fog = self.grade(self.gunning_fog)
self.linsear_write_formula = textstat.linsear_write_formula(strText)
self.str_linsear_write_formula = self.grade(self.linsear_write_formula)
self.smog_index = textstat.smog_index(strText)
self.str_smog_index = self.grade(self.smog_index)
self.text_standard = textstat.text_standard(strText)
def getWordComplexityScore(self,tokens, i): # A higher score means a document takes a higher education level to read if (i == 1): score = textstat.gunning_fog(tokens) elif (i == 2): # Texts of fewer than 30 sentences are statistically invalid, because the SMOG formula was normed on 30-sentence samples. # textstat requires atleast 3 sentences per article for a result. score = textstat.smog_index(tokens) else: score = textstat.flesch_kincaid_grade(tokens) return score
def calc_readby(sents_series0):
fogIndex=[]; flesch_kincaid=[]; flesch_readby=[];
for i0 in range(len(sents_series0)):
sent0 = sents_series0[i0]
flesch_readby.append(textstat.flesch_reading_ease(sent0))
flesch_kincaid.append(textstat.flesch_kincaid_grade(sent0))
fogIndex.append(textstat.gunning_fog(sent0))
if i0%10000==0:
print(i0)
df_readby = pd.DataFrame({'flesch_readby':flesch_readby, 'flesch_kincaid':flesch_kincaid, 'fogIndex':fogIndex})
return(df_readby)
def process(self, df):
t0 = time()
print("\n---Generating Readability Features:---\n")
def lexical_diversity(text):
words = nltk.tokenize.word_tokenize(text.lower())
word_count = len(words)
vocab_size = len(set(words))
diversity_score = vocab_size / word_count
return diversity_score
def get_counts(text, word_list):
words = nltk.tokenize.word_tokenize(text.lower())
count = 0
for word in words:
if word in word_list:
count += 1
return count
df['flesch_reading_ease'] = df['articleBody'].map(lambda x: textstat.flesch_reading_ease(x))
df['smog_index'] = df['articleBody'].map(lambda x: textstat.smog_index(x))
df['flesch_kincaid_grade'] = df['articleBody'].map(lambda x: textstat.flesch_kincaid_grade(x))
df['coleman_liau_index'] = df['articleBody'].map(lambda x: textstat.coleman_liau_index(x))
df['automated_readability_index'] = df['articleBody'].map(lambda x: textstat.automated_readability_index(x))
df['dale_chall_readability_score'] = df['articleBody'].map(lambda x: textstat.dale_chall_readability_score(x))
df['difficult_words'] = df['articleBody'].map(lambda x: textstat.difficult_words(x))
df['linsear_write_formula'] = df['articleBody'].map(lambda x: textstat.linsear_write_formula(x))
df['gunning_fog'] = df['articleBody'].map(lambda x: textstat.gunning_fog(x))
df['i_me_myself'] = df['articleBody'].apply(get_counts,args = (['i', 'me', 'myself'],))
df['punct'] = df['articleBody'].apply(get_counts,args = ([',','.', '!', '?'],))
df['lexical_diversity'] = df['articleBody'].apply(lexical_diversity)
feats = ['flesch_reading_ease', 'smog_index', 'flesch_kincaid_grade',
'coleman_liau_index', 'automated_readability_index',
'dale_chall_readability_score', 'difficult_words', 'linsear_write_formula',
'gunning_fog', 'i_me_myself', 'punct', 'lexical_diversity'
]
outfilename_xReadable = df[feats].values
with open('../saved_data/read.pkl', 'wb') as outfile:
pickle.dump(feats, outfile, -1)
pickle.dump(outfilename_xReadable, outfile, -1)
print ('readable features saved in read.pkl')
print('\n---Readability Features is complete---')
print("Time taken {} seconds\n".format(time() - t0))
return 1
def readability_scores(self, text):
self.ari = textstat.automated_readability_index(text)
self.flesch_kincaid_grade = textstat.flesch_kincaid_grade(text)
self.coleman_liau_index = textstat.coleman_liau_index(text)
self.dale_chall_readability_score = textstat.dale_chall_readability_score(
text)
self.flesch_reading_ease = textstat.flesch_reading_ease(text)
self.gunning_fog = textstat.gunning_fog(text)
self.linsear_write_formula = textstat.linsear_write_formula(text)
self.lix = textstat.lix(text)
self.rix = textstat.rix(text)
self.smog_index = textstat.smog_index(text)
self.text_standard = textstat.text_standard(text)
def update_readability_metrics(row, sentence):
# Update Word Count
sheet.update_cell(row, 6, (len(sentence.split()))),
time.sleep(1)
# Update Flesch Reading Ease
sheet.update_cell(
row, 7, (textstat.flesch_reading_ease(sheet.cell(row, 2).value))),
time.sleep(1)
# Update Gunning Fog Index
sheet.update_cell(row, 8,
(textstat.gunning_fog(sheet.cell(row, 2).value))),
time.sleep(1)
def get_readability_stats(text):
return {
'flesch_reading_ease': textstat.flesch_reading_ease(text),
'smog_index': textstat.smog_index(text),
'flesch_kincaid_grade': textstat.flesch_kincaid_grade(text),
'coleman_liau_index': textstat.coleman_liau_index(text),
'automated_readability_index':
textstat.automated_readability_index(text),
'dale_chall_readability_score':
textstat.dale_chall_readability_score(text),
'linsear_write_formula': textstat.linsear_write_formula(text),
'gunning_fog': textstat.gunning_fog(text),
'text_standard': textstat.text_standard(text, float_output=True),
}
def lisibilty(text):
f_lis = ([
textstat.syllable_count(str(text), lang='en_arabic'),
textstat.lexicon_count(str(text), removepunct=True),
textstat.sentence_count(str(text)),
textstat.flesch_reading_ease(str(text)),
textstat.flesch_kincaid_grade(str(text)),
textstat.gunning_fog(str(text)),
textstat.smog_index(str(text)),
textstat.automated_readability_index(str(text)),
textstat.coleman_liau_index(str(text)),
textstat.linsear_write_formula(str(text)),
textstat.dale_chall_readability_score(str(text))
])
return f_lis
def vocab_check(text):
#Construct dictionary
vocab_results = {'dale_chall_readability_score': dale_chall_readability_score(text),
'smog_index': smog_index(text), 'gunning_fog': gunning_fog(text),
'flesch_reading_ease': flesch_reading_ease(text),
'flesch_kincaid_grade': flesch_kincaid_grade(text),
'linsear_write_formula': linsear_write_formula(text),
'coleman_liau_index': coleman_liau_index(text),
'automated_readability_index': automated_readability_index(text),
'yule_vocab_richness': yule(text),
'total_score': text_standard(text, float_output=True)}
diff_words, easy_word_dict = difficult_words(text)
return(vocab_results, diff_words, easy_word_dict)
def analyze_vocab(text):
return {
'num_words': textstat.lexicon_count(text),
'flesch_reading_ease': textstat.flesch_reading_ease(text),
'smog_index': textstat.smog_index(text),
'flesch_kincaid_grade': textstat.flesch_kincaid_grade(text),
'coleman_liau_index': textstat.coleman_liau_index(text),
'automated_readability_index':
textstat.automated_readability_index(text),
'dale_chall_readability_score':
textstat.dale_chall_readability_score(text),
'difficult_words': textstat.difficult_words(text),
'linsear_write_formula': textstat.linsear_write_formula(text),
'gunning_fog': textstat.gunning_fog(text),
'text_standard': textstat.text_standard(text, float_output=True)
}
def textstat_stats(text):
difficulty = textstat.flesch_reading_ease(text)
grade_difficulty = textstat.flesch_kincaid_grade(text)
gfog = textstat.gunning_fog(text)
smog = textstat.smog_index(text)
ari = textstat.automated_readability_index(text)
cli = textstat.coleman_liau_index(text)
lwf = textstat.linsear_write_formula(text)
dcrs = textstat.dale_chall_readability_score(text)
idx = [
'difficulty', 'grade_difficulty', 'gfog', 'smog', 'ari', 'cli', 'lwf',
'dcrs'
]
return pd.Series(
[difficulty, grade_difficulty, gfog, smog, ari, cli, lwf, dcrs],
index=idx)
def test_gunning_fog():
score = textstat.gunning_fog(long_test)
assert score == 11.26
