Machine learning 值错误：应为原始文本文档上的Iterable，收到字符串对象。（TFIDF（矢量化））

我已经尝试了很多方法来解决这个问题，但仍然会出现错误。我不知道为什么 #导入的库

import pandas as pd
import numpy as np
import nltk
import sklearn
from sklearn import svm
from sklearn.model_selection import train_test_split
from nltk.probability import FreqDist
from collections import Counter
from nltk import word_tokenize,sent_tokenize
from nltk.corpus import stopwords
from nltk.stem import WordNetLemmatizer
import matplotlib.pyplot as pt
import string 
from wordcloud import WordCloud
from PIL import Image
from nltk import pos_tag
from sklearn.feature_extraction.text import TfidfVectorizer
import re
from nltk.corpus import wordnet
from nltk.tag import pos_tag#Libraries imported
import pandas as pd
import numpy as np
import nltk
import sklearn
from sklearn import svm
from sklearn.model_selection import train_test_split
from nltk.probability import FreqDist
from collections import Counter
from nltk import word_tokenize,sent_tokenize
from nltk.corpus import stopwords
from nltk.stem import WordNetLemmatizer
import matplotlib.pyplot as pt
import string 
from wordcloud import WordCloud
from PIL import Image
from nltk import pos_tag
from sklearn.feature_extraction.text import TfidfVectorizer
import re
from nltk.corpus import wordnet
from nltk.tag import pos_tag

TR_DS=pd.read_csv("D:\DataSet\Training_Dataset.csv")
TR_DS.drop(["title","date","subject"],axis=1,inplace=True)#Delete unnecessary columns
TR_DS.isnull().any()
TR_DS.head()

#text column with small letters
news_text=TR_DS['text'].str.lower()

#Remove Punctuations
news_text=news_text.str.replace(r'[^\w\d\s]',' ')

#Remove Digits
news_text=news_text.str.replace('\d+', '')
        
#Tokenization (Text as Words)
def tokenize(text):
    tokens= re.split('\W+',text)  
    return tokens
news_text=news_text.apply(lambda x : word_tokenize(x.lower()))  

#Remove StopWords
stop_words=stopwords.words("english")
def remove_stopwords(text):
    text = [word for word in text if word not in stop_words]
    return text
news_text=news_text.apply(lambda x: remove_stopwords(x))

#Lemmatization Words in Stem Form
WordNetLemmatizer = nltk.WordNetLemmatizer()
def lemmer(text):
    text = [WordNetLemmatizer.lemmatize(word, pos='v') for word in text]
    return text
news_text=news_text.apply(lambda x: lemmer(x))
print(news_text)

输出

 0 [reuters, man, accuse, tackle, u, senator, ran... 1      [washington, reuters, republican, u, senators,... 2      [reuters, proposals, u, republicans, repeal, r... 3      [reuters, tax, plan, u, senate, republicans, r... 4      [washington, reuters, americans, likely, belie...
                             ...                         295    [donald, trump, kick, hispanic, heritage, mont... 296    [us, even, conservatives, know, donald, trump,... 297    [take, office, donald, trump, repeatedly, take... 298    [sunday, august, th, salt, lake, city, police,... 299    [u, rep, tim, murphy, staunch, pro, life, repu... Name: text, Length: 300, dtype: object
tfidf_vect = TfidfVectorizer(max_features=5000,  ngram_range = (1,2))
def TFIDF(text):
    text = [tfidf_vect.fit(word) for word in text]
    return text
news_text=news_text.apply(lambda x: TFIDF(x))

**错误

ValueError                                Traceback (most recent call last) <ipython-input-232-886e227564fb> in <module>
      4     text = [tfidf_vect.fit(word) for word in text]
      5     return text
----> 6 news_text=news_text.apply(lambda x: TFIDF(x))

c:\users\mahad maqsood\appdata\local\programs\python\python39\lib\site-packages\pandas\core\series.py in apply(self, func, convert_dtype, args, **kwds)    4210             else:    4211                 values = self.astype(object)._values
-> 4212                 mapped = lib.map_infer(values, f, convert=convert_dtype)    4213     4214         if len(mapped) and isinstance(mapped[0], Series):

pandas\_libs\lib.pyx in pandas._libs.lib.map_infer()

<ipython-input-232-886e227564fb> in <lambda>(x)
      4     text = [tfidf_vect.fit(word) for word in text]
      5     return text
----> 6 news_text=news_text.apply(lambda x: TFIDF(x))

<ipython-input-232-886e227564fb> in TFIDF(text)
      2 tfidf_vect = TfidfVectorizer()
      3 def TFIDF(text):
----> 4     text = [tfidf_vect.fit(word) for word in text]
      5     return text
      6 news_text=news_text.apply(lambda x: TFIDF(x))

<ipython-input-232-886e227564fb> in <listcomp>(.0)
      2 tfidf_vect = TfidfVectorizer()
      3 def TFIDF(text):
----> 4     text = [tfidf_vect.fit(word) for word in text]
      5     return text
      6 news_text=news_text.apply(lambda x: TFIDF(x))

c:\users\mahad maqsood\appdata\local\programs\python\python39\lib\site-packages\sklearn\feature_extraction\text.py in fit(self, raw_documents, y)    1816         self._check_params()    1817         self._warn_for_unused_params()
-> 1818         X = super().fit_transform(raw_documents)    1819         self._tfidf.fit(X)    1820         return self

c:\users\mahad maqsood\appdata\local\programs\python\python39\lib\site-packages\sklearn\feature_extraction\text.py in fit_transform(self, raw_documents, y)    1186         # TfidfVectorizer.    1187         if isinstance(raw_documents, str):
-> 1188             raise ValueError(    1189                 "Iterable over raw text documents expected, "    1190                 "string object received.")

ValueError: Iterable over raw text documents expected, string object received.

ValueError回溯（最后一次最近调用）
4 text=[tfidf_vect.fit（word）表示文本中的单词]
5返回文本
---->6新闻文本=新闻文本。应用（lambda x:TFIDF（x））
c:\users\mahad maqsood\appdata\local\programs\python 39\lib\site packages\pandas\core\series.py in apply（self，func，convert\u dtype，args，**kwds）4210-else:4211-values=self.astype（object）。\u-values
->4212 mapped=lib.map\u推断（值，f，convert=convert\u dtype）4213 4214如果len（映射）和isinstance（映射[0]，序列）：
熊猫\\u libs\lib.pyx在熊猫中。_libs.lib.map\u infere（）
in（x）
4 text=[tfidf_vect.fit（word）表示文本中的单词]
5返回文本
---->6新闻文本=新闻文本。应用（lambda x:TFIDF（x））
在TFIDF中（文本）
2 tfidf_vect=TFIDFvectorier（）
3 def TFIDF（文本）：
---->4 text=[tfidf_vect.fit（word）表示文本中的单词]
5返回文本
6新闻文本=新闻文本。应用（lambda x:TFIDF（x））
英寸（.0）
2 tfidf_vect=TFIDFvectorier（）
3 def TFIDF（文本）：
---->4 text=[tfidf_vect.fit（word）表示文本中的单词]
5返回文本
6新闻文本=新闻文本。应用（lambda x:TFIDF（x））
c:\users\mahad maqsood\appdata\local\programs\python\39\lib\site packages\sklearn\feature\u extraction\text.py in fit（self，raw\u documents，y）1816 self.\u check\u params（）1817 self.\u warn\u未使用的参数（）
->1818 X=super（）.fit\u转换（原始文档）1819 self.\u tfidf.fit（X）1820返回self
c:\users\mahad maqsood\appdata\local\programs\python\python39\lib\site packages\sklearn\feature\u extraction\text.py in fit_transform（self，raw_documents，y）1186#tfidfvectorier。1187如果存在（原始文档，str）：
->1188 raise VALUERROR（预期1189“Iterable超过原始文本文档”，接收到1190“字符串对象”）
ValueError:Iterable超过原始文本文档，收到字符串对象。