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我試圖在NLTK電影運行和實例CountVectorizer()評論文集,使用下面的代碼:CountVectorizer():StreamBackedCorpusView」對象有沒有屬性‘低’
>>>import nltk
>>>import nltk.corpus
>>>from sklearn.feature_extraction.text import CountVectorizer
>>>from nltk.corpus import movie_reviews
>>>neg_rev = movie_reviews.fileids('neg')
>>>pos_rev = movie_reviews.fileids('pos')
>>>rev_list = [] # Empty List
>>>for rev in neg_rev:
rev_list.append(nltk.corpus.movie_reviews.words(rev))
>>>for rev_pos in pos_rev:
rev_list.append(nltk.corpus.movie_reviews.words(rev_pos))
>>>count_vect = CountVectorizer()
>>>X_count_vect = count_vect.fit_transform(rev_list)
我收到以下錯誤:
AttributeError Traceback (most recent call last)
<ipython-input-37-00e9047daa67> in <module>()
----> 1 X_count_vect = count_vect.fit_transform(rev_list)
C:\ProgramData\Anaconda3\lib\site-packages\sklearn\feature_extraction\text.py in fit_transform(self, raw_documents, y)
837
838 vocabulary, X = self._count_vocab(raw_documents,
--> 839 self.fixed_vocabulary_)
840
841 if self.binary:
C:\ProgramData\Anaconda3\lib\site-packages\sklearn\feature_extraction\text.py in _count_vocab(self, raw_documents, fixed_vocab)
760 for doc in raw_documents:
761 feature_counter = {}
--> 762 for feature in analyze(doc):
763 try:
764 feature_idx = vocabulary[feature]
C:\ProgramData\Anaconda3\lib\site-packages\sklearn\feature_extraction\text.py in <lambda>(doc)
239
240 return lambda doc: self._word_ngrams(
--> 241 tokenize(preprocess(self.decode(doc))), stop_words)
242
243 else:
C:\ProgramData\Anaconda3\lib\site-packages\sklearn\feature_extraction\text.py in <lambda>(x)
205
206 if self.lowercase:
--> 207 return lambda x: strip_accents(x.lower())
208 else:
209 return strip_accents
AttributeError: 'StreamBackedCorpusView' object has no attribute 'lower'
nltk.corpus.movie_reviews.words(rev_pos)
已標記化的句子....如:
['films', 'adapted', 'from', 'comic', 'books', 'have', ...]
任何人都可以請告訴我我做錯了什麼?我假設我在創建電影評論的(rev_list)
列表中進行了一些嘗試。
TIA
您應該檢查類型'nltk.corpus.movie_reviews.words(rev_pos)'你是追加到列表中。它應該是一個由CountVectorizer處理的字符串,我不認爲它是當前的。 –