def create_blob(self):
"""
hier kommt HanTa
"""
print(".")
print(' >>> HanoverTagger GermaLemma with TigerCorpus <<<')
tagger = ht.HanoverTagger('morphmodel_ger.pgz')
#
# Siehe: Christian Wartena (2019). A Probabilistic Morphology Model for German Lemmatization.
# In: Proceedings of the 15th Conference on Natural Language Processing (KONVENS 2019): Long Papers. Pp. 40-49, Erlangen. )
#
def convert(lst):
return ' '.join(lst).split()
#
pepe = convert(self.clean_data)
tags = tagger.tag_sent(pepe)
#
lemma_list = []
for item in tags:
lemma_list.append(item[1])
#
self.hanta_lemma = lemma_list
# print(lemma_list)
#
blob_wtf = tbde(str(self.clean_data))
# blob_wtf.words.singularize()
self.blob_lemma = _lemmatizer.lemmatize(str(blob_wtf))
self.blob_polarity = tbde(str(self.blob_lemma))
# blob_wtf.parse()
print(" -/- ")
# print(" TF-IDF Auswertung ")
return self.blob_lemma, self.blob_polarity, self.hanta_lemma
def tfidf_calculate(self):
"""
Erechnet den Tfidf von in jeweils 1/3 geteilte Abschnitte des sliste_n
"""
self.sliste_n = [x for (x,y) in self.blob_lemma if y not in ('N')]
def tf(word, blob):
return blob.words.count(word) / len(blob.words)
def n_containing(word, bloblist):
return sum(1 for blob in bloblist if word in blob.words)
def idf(word, bloblist):
return math.log(len(bloblist) / (1 + n_containing(word, bloblist)))
def tfidf(word, blob, bloblist):
return tf(word, blob) * idf(word, bloblist)
nb1 = int(len(self.clean_data) * 0.333)
nb2 = nb1 * 2
nb3 = len(self.clean_data)
doku1= tbde(str(self.sliste_n[0:nb1]))
doku2= tbde(str(self.sliste_n[nb1:nb2]))
doku3= tbde(str(self.sliste_n[nb2:nb3]))
bloblist = [doku1,doku2,doku3]
for i, item in enumerate(bloblist):
print("Top words in document {}".format(i + 1))
scores = {word: tfidf(word, item, bloblist) for word in item.words}
sorted_words = sorted(scores.items(), key=lambda x: x[1], reverse=True)
for element, score in sorted_words[:3]:
print("\tWord: {}, TF-IDF: {}".format(element, round(score, 4)))
return self.sliste_n
def create_blob(self):
"""
Erzeugt blob objekte: 1x blob lemma liste und 1x pures blob object
"""
blob_wtf = tbde(str(self.clean_data))
self.blob_lemma = _lemmatizer.lemmatize(str(blob_wtf))
self.blob_polarity = tbde(str(self.blob_lemma))
return self.blob_lemma, self.blob_polarity
