问题
I'm looking for an effective way to construct a Term Document Matrix in Python that can be used together with extra data.
I have some text data with a few other attributes. I would like to run some analyses on the text and I would like to be able to correlate features extracted from text (such as individual word tokens or LDA topics) with the other attributes.
My plan was load the data as a pandas data frame and then each response will represent a document. Unfortunately, I ran into an issue:
import pandas as pd
import nltk
pd.options.display.max_colwidth = 10000
txt_data = pd.read_csv("data_file.csv",sep="|")
txt = str(txt_data.comment)
len(txt)
Out[7]: 71581
txt = nltk.word_tokenize(txt)
txt = nltk.Text(txt)
txt.count("the")
Out[10]: 45
txt_lines = []
f = open("txt_lines_only.txt")
for line in f:
txt_lines.append(line)
txt = str(txt_lines)
len(txt)
Out[14]: 1668813
txt = nltk.word_tokenize(txt)
txt = nltk.Text(txt)
txt.count("the")
Out[17]: 10086
Note that in both cases, text was processed in such a way that only the anything but spaces, letters and ,.?! was removed (for simplicity).
As you can see a pandas field converted into a string returns fewer matches and the length of the string is also shorter.
Is there any way to improve the above code?
Also, str(x) creates 1 big string out of the comments while [str(x) for x in txt_data.comment] creates a list object which cannot be broken into a bag of words. What is the best way to produce a nltk.Text object that will retain document indices? In other words I'm looking for a way to create a Term Document Matrix, R's equivalent of TermDocumentMatrix() from tm package.
Many thanks.
回答1:
The benefit of using a pandas DataFrame would be to apply the nltk functionality to each row like so:
word_file = "/usr/share/dict/words"
words = open(word_file).read().splitlines()[10:50]
random_word_list = [[' '.join(np.random.choice(words, size=1000, replace=True))] for i in range(50)]
df = pd.DataFrame(random_word_list, columns=['text'])
df.head()
text
0 Aaru Aaronic abandonable abandonedly abaction ...
1 abampere abampere abacus aback abalone abactor...
2 abaisance abalienate abandonedly abaff abacina...
3 Ababdeh abalone abac abaiser abandonable abact...
4 abandonable abandon aba abaiser abaft Abama ab...
len(df)
50
txt = df.text.apply(word_tokenize)
txt.head()
0 [Aaru, Aaronic, abandonable, abandonedly, abac...
1 [abampere, abampere, abacus, aback, abalone, a...
2 [abaisance, abalienate, abandonedly, abaff, ab...
3 [Ababdeh, abalone, abac, abaiser, abandonable,...
4 [abandonable, abandon, aba, abaiser, abaft, Ab...
txt.apply(len)
0 1000
1 1000
2 1000
3 1000
4 1000
....
44 1000
45 1000
46 1000
47 1000
48 1000
49 1000
Name: text, dtype: int64
As a result, you get the .count() for each row entry:
txt = txt.apply(lambda x: nltk.Text(x).count('abac'))
txt.head()
0 27
1 24
2 17
3 25
4 32
You can then sum the result using:
txt.sum()
1239
来源：https://stackoverflow.com/questions/34784004/python-text-processing-nltk-and-pandas