I am building a word frequency, and relative frequency, list(s) for a collection of text files. Having discovered, by hand, that a couple of texts can overly influence the frequency of a word, one of the things I want to be able to do is count the number of times a word occurs. It strikes me that there are two ways to do this:
First, to compile a word frequency dictionary (as below -- and I'm not using the NLTK FreqDist because this code actually runs more quickly but if FreqDist has the above functionality built-in and I just didn't know it, I'll take it):
import nltk
tokenizer = nltk.tokenize.RegexpTokenizer(r'\w+')
freq_dic = {}
for text in ftexts:
words = tokenizer.tokenize(text)
for word in words:
# form dictionary
try:
freq_dic[word] += 1
except:
freq_dic[word] = 1
From there, I assume I'll need to write another loop that uses the keys above as keywords:
# This is just scratch code
for text in ftexts:
while True:
if keyword not in line:
continue
else:
break
count = count + 1
And then I'll find some way to mesh these two dictionaries into a tuple or, possibly, a pandas dataframe by word, such that:
word1, frequency, # of texts in which it occurs
word2, frequency, # of texts in which it occurs
The other thing that occurred to me as I was writing this question was to use SciKit's term frequency matrix and then count rows in which a word occurs? Is that possible?
ADDED TO CLARIFY:
Imagine three sentences: ["I need to keep count of the children.", "If you want to know what the count is, just ask." "There is nothing here but chickens, chickens, chickens."]
"count" occurs 2x but is in two different texts; "chickens" occurs three times, but is in only one text. What I want is a report that looks like this:
WORD, FREQ, TEXTS
count, 2, 2
chicken, 3, 1
