Gensim - TF-IDF，如何执行正确的 Genesis TF-IDF？

如何解决Gensim - TF-IDF，如何执行正确的 Genesis TF-IDF？

我正在尝试在我的学士论文的一部分中执行一些 NLP（更准确地说是一个 TF-IDF 项目）。

我将其中的一小部分导出到名为“thesis.txt”的单个文档中，似乎在将清理过的文本数据拟合到 gensim 字典时遇到了问题。

所有的词都被标记化，存储在一个词袋中，我无法弄清楚我做错了什么。

这是我得到的错误：

    ---------------------------------------------------------------------------
TypeError                                 Traceback (most recent call last)
<ipython-input-317-73828cccaebe> in <module>
     17 
     18 #Create dictionary
---> 19 dictionary = Dictionary(tokens_no_stop)
     20 
     21 #Create bag of words

~/Library/Python/3.8/lib/python/site-packages/gensim/corpora/dictionary.py in __init__(self,documents,prune_at)
     89 
     90         if documents is not None:
---> 91             self.add_documents(documents,prune_at=prune_at)
     92 
     93     def __getitem__(self,tokenid):

~/Library/Python/3.8/lib/python/site-packages/gensim/corpora/dictionary.py in add_documents(self,prune_at)
    210 
    211             # update Dictionary with the document
--> 212             self.doc2bow(document,allow_update=True)  # ignore the result,here we only care about updating token ids
    213 
    214         logger.info(

~/Library/Python/3.8/lib/python/site-packages/gensim/corpora/dictionary.py in doc2bow(self,document,allow_update,return_missing)
    250         """
    251         if isinstance(document,string_types):
--> 252             raise TypeError("doc2bow expects an array of unicode tokens on input,not a single string")
    253 
    254         # Construct (word,frequency) mapping.

TypeError: doc2bow expects an array of unicode tokens on input,not a single string

预先感谢您的帮助:)（在我的代码下方找到）

from nltk import word_tokenize
from nltk.corpus import stopwords
from nltk.stem import WordNetLemmatizer
from collections import Counter
from gensim.corpora import Dictionary
from gensim.models.tfidfmodel import TfidfModel

f = open('/Users/romeoleon/Desktop/Python & R/NLP/TRIAL_THESIS/thesis.txt','r')
text = f.read()

#Tokenize text
Tokens = word_tokenize(text)

#Lower case everything
Tokens = [t.lower() for t in Tokens]

#Keep only leters
tokens_alpha = [t for t in Tokens if t.isalpha()]

#Remove stopwords
tokens_no_stop = [t for t in tokens_alpha if t not in stopwords.words('french')]

#Create Lemmatizer
lem = WordNetLemmatizer()
lemmatized = [lem.lemmatize(t) for t in tokens_no_stop]


#Create dictionary
dictionary = Dictionary(tokens_no_stop)

#Create bag of words
bow = [dictionary.doc2bow(line) for line in tokens_no_stop]

#Model TFID
tfidf = TfidfModel(bow)
bow_tfidf = tfidf[bow]

解决方法

您的 tokens_no_stop 是一个字符串列表，但 Dictionary 需要一个字符串列表列表（更准确地说，是一个可迭代的字符串列表）。