def detect(self, text):
'''
Detect the text's language
'''
words = nltk_word_tokenize(text.lower())
trigrams = {}
scores = dict([(lang, 0) for lang in self.language_trigrams.keys()])
for match in words:
for trigram in self.get_word_trigrams(match):
if not trigram in trigrams.keys():
trigrams[trigram] = 0
trigrams[trigram] += 1
total = sum(trigrams.values())
for trigram, count in trigrams.items():
for lang, frequencies in self.language_trigrams.items():
# normalize and add to the total score
scores[lang] += (float(frequencies[trigram]) / float(
frequencies.N())) * (float(count) / float(total))
# special case
# if all scores are 0.0 we return None
s = 0.0
for score in scores.itervalues():
s += score
if s == 0.0:
return None
return l_map[sorted(scores.items(), key=lambda x: x[1],
reverse=True)[0][0]]
def detect(self, text):
'''
Detect the text's language
'''
words = nltk_word_tokenize(text.lower())
trigrams = {}
scores = dict([(lang, 0) for lang in self.language_trigrams.keys()])
for match in words:
for trigram in self.get_word_trigrams(match):
if not trigram in trigrams.keys():
trigrams[trigram] = 0
trigrams[trigram] += 1
total = sum(trigrams.values())
for trigram, count in trigrams.items():
for lang, frequencies in self.language_trigrams.items():
# normalize and add to the total score
scores[lang] += (float(frequencies[trigram]) / float(frequencies.N())) * (float(count) / float(total))
# special case
# if all scores are 0.0 we return None
s = 0.0
for score in scores.itervalues():
s += score
if s == 0.0:
return None
return l_map[ sorted(scores.items(), key=lambda x: x[1], reverse=True)[0][0] ]
def preprocess_text_nltk(self, text):
sentences = self.sent_tokenize(text)
sentences_cleaned = []
for sent in sentences:
if self.stopwords_remove:
self.stopword_remover.replace_keywords(sent)
words = nltk_word_tokenize(sent, self.language)
words = [w for w in words if w not in string.punctuation]
words = [w for w in words if w not in self.extra_stopwords]
words = [w.lower() for w in words]
sentences_cleaned.append(" ".join(words))
return sentences_cleaned
def word_tokenize(sentence, pt_tokenizer=True):
"""Tokenize sentence into words.
NOTE: Default is `TreebankWordTokenizer`.
If pt_tokenizer, use `TreebankWordTokenizer`, don't work well for "can't"
TODO(zcq)
"""
if pt_tokenizer:
return nltk_word_tokenize(sentence)
else:
return nltk_WP_tokenize(sentence)
def word_tokenize(sentence, tokenizer="nltk"):
"""
Tokenize the input string.
Args:
sentence (string): The input string
tokenizer (string): The tokenizer to use. Default is nltk word tokenizer
Returns:
List[string]: The tokens from the input string
"""
if tokenizer in "nltk":
return nltk_word_tokenize(sentence)
else:
return re.split(SENTENCE_SPLIT_REGEX, sentence)
def detect(self, text):
'''
Detect the text's language
'''
#print "Detect: " + text
try:
self.__mutex.acquire()
if not text:
raise ValueError(u"Text: " + unicode(text))
text = unicodedata.normalize("NFC", text)
words = nltk_word_tokenize(text.lower())
trigrams = {}
scores = dict([(lang, 0) for lang in self.language_trigrams.keys()])
for match in words:
word_trigrams = self.__get_word_trigrams(match)
#print "Match: " + match
#print "trigrams: " + str(word_trigrams)
for trigram in word_trigrams:
if not trigram in trigrams.keys():
trigrams[trigram] = 0
trigrams[trigram] += 1
total = sum(trigrams.values())
for trigram, count in trigrams.items():
for lang, frequencies in self.language_trigrams.items():
# normalize and add to the total score
try:
scores[lang] += (float(frequencies[trigram]) / float(frequencies.N())) * (float(count) / float(total))
except ZeroDivisionError as e:
logger.error(u"Div: " + unicode(float(frequencies.N())) + u" " + unicode(float(total)))
raise e
sorted_scores = sorted(scores.items(), key=lambda x: x[1], reverse=True)
#print sorted_scores
#logger.info(u"%s: %s" % (text, unicode(sorted_scores)))
for lang, score in sorted_scores:
if score > 0.0001:
return lang
return None
finally:
self.__mutex.release()
def preprocess_text_nltk(text):
stopwords_remove = True
language = 'english'
sentences = sent_tokenize(text)
extra_stopwords = ["''", "``", "'s"]
sentences_cleaned = []
if stopwords_remove:
stopword_remover = flashtext.KeywordProcessor()
for stopword in stopwords.words(language):
stopword_remover.add_keyword(stopword, '')
stopword_remover = stopword_remover
for sent in sentences:
if stopwords_remove:
stopword_remover.replace_keywords(sent)
words = nltk_word_tokenize(sent, language)
words = [w for w in words if w not in string.punctuation]
words = [w for w in words if w not in extra_stopwords]
words = [w.lower() for w in words]
sentences_cleaned.append(" ".join(words))
return sentences_cleaned
def detect(self, text):
words = nltk_word_tokenize(text.lower())
trigrams = {}
scores = dict([(lang, 0) for lang in self.language_trigrams.keys()])
trigcount = [(trigram, 1.0) for match in words for trigram in self.get_word_trigrams(match)]
if len(trigcount) > 0:
trigdf = pandas.DataFrame(trigcount, columns = ["key", "value"])
trigrams = trigdf.groupby("key")["value"].sum().to_dict()
else:
trigrams = {}
total = sum(trigrams.values())
maxscore, maxid = 0, ""
for trigram, count in trigrams.items():
trishare = (float(count) / float(total))
for lang, frequencies in filter(lambda (l, f): trigram in f, self.language_dicts.iteritems()):
scores[lang] += frequencies[trigram] * trishare
if scores[lang] > maxscore:
maxid, maxscore = lang, scores[lang]
def mapper(self, key, tweet):
'''
Detect the text's language
'''
obj = cjson.decode(tweet)
text = obj['tx']
words = nltk_word_tokenize(text.lower())
trigrams = {}
scores = dict([(lang, 0) for lang in self.language_trigrams.keys()])
for match in words:
for trigram in self.get_word_trigrams(match):
if not trigram in trigrams.keys():
trigrams[trigram] = 0
trigrams[trigram] += 1
total = sum(trigrams.values())
for trigram, count in trigrams.items():
for lang, frequencies in self.language_trigrams.items():
# normalize and add to the total score
scores[lang] += (float(frequencies[trigram]) / float(frequencies.N())) * (float(count) / float(total))
obj['lang'] = sorted(scores.items(), key=lambda x: x[1], reverse=True)[0][0]
yield key, obj
def detect(self, text):
#tokenize the words
words = nltk_word_tokenize(text.lower())
trigrams = {}
scores = dict([(lang, 0) for lang in self.language_trigrams.keys()])
#get the trigrams and insert count of trigrams in a list
for match in words:
for trigram in self.get_word_trigrams(match):
if not trigram in trigrams.keys():
trigrams[trigram] = 0
trigrams[trigram] += 1
total = sum(trigrams.values())
#normalie the frequency and sort according to the keys.
for trigram, count in trigrams.items():
for lang, frequencies in self.language_trigrams.items():
# normalize and add to the total score
scores[lang] += (float(frequencies[trigram]) / float(frequencies.N())) * (float(count) / float(total))
return sorted(scores.items(), key=lambda x: x[1], reverse=True)[0][0]
def mapper(self, key, tweet):
'''
Detect the text's language
'''
obj = cjson.decode(tweet)
text = obj['tx']
words = nltk_word_tokenize(text.lower())
trigrams = {}
scores = dict([(lang, 0) for lang in self.language_trigrams.keys()])
for match in words:
for trigram in self.get_word_trigrams(match):
if not trigram in trigrams.keys():
trigrams[trigram] = 0
trigrams[trigram] += 1
total = sum(trigrams.values())
for trigram, count in trigrams.items():
for lang, frequencies in self.language_trigrams.items():
# normalize and add to the total score
scores[lang] += (float(frequencies[trigram]) / float(
frequencies.N())) * (float(count) / float(total))
obj['lang'] = sorted(scores.items(), key=lambda x: x[1],
reverse=True)[0][0]
yield key, obj
