Ответ 1

Благодаря Радиму и Ларсмансу. Моя цель состояла в том, чтобы иметь DTM, как тот, который вы получаете в R tm. Я решил использовать scikit-learn и частично вдохновлен этой записью в блоге. Этот код я придумал.

Я размещаю его здесь в надежде, что кто-то другой найдет его полезным.

import pandas as pd
from sklearn.feature_extraction.text import CountVectorizer 

def fn_tdm_df(docs, xColNames = None, **kwargs):
    ''' create a term document matrix as pandas DataFrame
    with **kwargs you can pass arguments of CountVectorizer
    if xColNames is given the dataframe gets columns Names'''

    #initialize the  vectorizer
    vectorizer = CountVectorizer(**kwargs)
    x1 = vectorizer.fit_transform(docs)
    #create dataFrame
    df = pd.DataFrame(x1.toarray().transpose(), index = vectorizer.get_feature_names())
    if xColNames is not None:
        df.columns = xColNames

    return df

чтобы использовать его в списке текста в каталоге

DIR = 'C:/Data/'

def fn_CorpusFromDIR(xDIR):
    ''' functions to create corpus from a Directories
    Input: Directory
    Output: A dictionary with 
             Names of files ['ColNames']
             the text in corpus ['docs']'''
    import os
    Res = dict(docs = [open(os.path.join(xDIR,f)).read() for f in os.listdir(xDIR)],
               ColNames = map(lambda x: 'P_' + x[0:6], os.listdir(xDIR)))
    return Res

для создания фрейма данных

d1 = fn_tdm_df(docs = fn_CorpusFromDIR(DIR)['docs'],
          xColNames = fn_CorpusFromDIR(DIR)['ColNames'], 
          stop_words=None, charset_error = 'replace')  

Ответ 2

Я знаю, что OP хотел создать tdm в NLTK, но пакет textmining (pip install textmining) делает его мертвым простым:

import textmining

def termdocumentmatrix_example():
    # Create some very short sample documents
    doc1 = 'John and Bob are brothers.'
    doc2 = 'John went to the store. The store was closed.'
    doc3 = 'Bob went to the store too.'
    # Initialize class to create term-document matrix
    tdm = textmining.TermDocumentMatrix()
    # Add the documents
    tdm.add_doc(doc1)
    tdm.add_doc(doc2)
    tdm.add_doc(doc3)
    # Write out the matrix to a csv file. Note that setting cutoff=1 means
    # that words which appear in 1 or more documents will be included in
    # the output (i.e. every word will appear in the output). The default
    # for cutoff is 2, since we usually aren't interested in words which
    # appear in a single document. For this example we want to see all
    # words however, hence cutoff=1.
    tdm.write_csv('matrix.csv', cutoff=1)
    # Instead of writing out the matrix you can also access its rows directly.
    # Let print them to the screen.
    for row in tdm.rows(cutoff=1):
            print row

termdocumentmatrix_example()

Вывод:

['and', 'the', 'brothers', 'to', 'are', 'closed', 'bob', 'john', 'was', 'went', 'store', 'too']
[1, 0, 1, 0, 1, 0, 1, 1, 0, 0, 0, 0]
[0, 2, 0, 1, 0, 1, 0, 1, 1, 1, 2, 0]
[0, 1, 0, 1, 0, 0, 1, 0, 0, 1, 1, 1]

В качестве альтернативы можно использовать pandas и sklearn [источник]:

import pandas as pd
from sklearn.feature_extraction.text import CountVectorizer

docs = ['why hello there', 'omg hello pony', 'she went there? omg']
vec = CountVectorizer()
X = vec.fit_transform(docs)
df = pd.DataFrame(X.toarray(), columns=vec.get_feature_names())
print(df)

Вывод:

   hello  omg  pony  she  there  went  why
0      1    0     0    0      1     0    1
1      1    1     1    0      0     0    0
2      0    1     0    1      1     1    0

Ответ 3

Альтернативный подход с использованием токенов и фрейма данных

import nltk
comment #nltk.download() to get toenize
from urllib import request
url = "http://www.gutenberg.org/files/2554/2554-0.txt"
response = request.urlopen(url)
raw = response.read().decode('utf8')
type(raw)

tokens = nltk.word_tokenize(raw)
type(tokens)

tokens[1:10]
['Project',
 'Gutenberg',
 'EBook',
 'of',
 'Crime',
 'and',
 'Punishment',
 ',',
 'by']

tokens2=pd.DataFrame(tokens)
tokens2.columns=['Words']
tokens2.head()


Words
0   The
1   Project
2   Gutenberg
3   EBook
4   of

    tokens2.Words.value_counts().head()
,                 16178
.                  9589
the                7436
and                6284
to                 5278