From most frequent words how to extract technical skill words
$begingroup$
I've scrape 30 job description web and stored them into a list called job_desc where each item is a job description.
# each item is a list of tokenized job_description
tok = [nltk.word_tokenize(job.lower()) for job in job_desc]
# ignore stop words, bullets, etc. And put it into one list
from nltk.corpus import stopwords
stop = stopwords.words('english')
def clean_token(what_to_clean):
cleaned_tok =
for lists in what_to_clean:
for item in lists:
if len(item)>2 and (item not in stop):
cleaned_tok.append(item)
return cleaned_tok
After cleaning job description I've found most frequent words using:
freq = nltk.FreqDist(clean_token(tok))
most_freq_words = freq.most_common(100)
Which outputs:
[('data', 211),
('experience', 78),
('learning', 70),
('business', 65),
('team', 53),
('science', 51),
('machine', 48),.....
From here I only want to extract words like machine, python, C+, technical skills. How can I go about it?
Also you can see there is word "machine" showing up 48 times and I am not sure whether it is talking about machine learning how can I go about this, I know if I want to make predictions I could've used CountVectorizer and n-grams.
python nltk
asked 10 mins ago
h_muskh_musk
61
$endgroup$
add a comment |
$begingroup$
I've scrape 30 job description web and stored them into a list called job_desc where each item is a job description.
# each item is a list of tokenized job_description
tok = [nltk.word_tokenize(job.lower()) for job in job_desc]
# ignore stop words, bullets, etc. And put it into one list
from nltk.corpus import stopwords
stop = stopwords.words('english')
def clean_token(what_to_clean):
cleaned_tok =
for lists in what_to_clean:
for item in lists:
if len(item)>2 and (item not in stop):
cleaned_tok.append(item)
return cleaned_tok
After cleaning job description I've found most frequent words using:
freq = nltk.FreqDist(clean_token(tok))
most_freq_words = freq.most_common(100)
Which outputs:
[('data', 211),
('experience', 78),
('learning', 70),
('business', 65),
('team', 53),
('science', 51),
('machine', 48),.....
From here I only want to extract words like machine, python, C+, technical skills. How can I go about it?
Also you can see there is word "machine" showing up 48 times and I am not sure whether it is talking about machine learning how can I go about this, I know if I want to make predictions I could've used CountVectorizer and n-grams.
python nltk
asked 10 mins ago
h_muskh_musk
61
$endgroup$
add a comment |
$begingroup$
I've scrape 30 job description web and stored them into a list called job_desc where each item is a job description.
# each item is a list of tokenized job_description
tok = [nltk.word_tokenize(job.lower()) for job in job_desc]
# ignore stop words, bullets, etc. And put it into one list
from nltk.corpus import stopwords
stop = stopwords.words('english')
def clean_token(what_to_clean):
cleaned_tok =
for lists in what_to_clean:
for item in lists:
if len(item)>2 and (item not in stop):
cleaned_tok.append(item)
return cleaned_tok
After cleaning job description I've found most frequent words using:
freq = nltk.FreqDist(clean_token(tok))
most_freq_words = freq.most_common(100)
Which outputs:
[('data', 211),
('experience', 78),
('learning', 70),
('business', 65),
('team', 53),
('science', 51),
('machine', 48),.....
From here I only want to extract words like machine, python, C+, technical skills. How can I go about it?
Also you can see there is word "machine" showing up 48 times and I am not sure whether it is talking about machine learning how can I go about this, I know if I want to make predictions I could've used CountVectorizer and n-grams.
python nltk
asked 10 mins ago
h_muskh_musk
61
$endgroup$
I've scrape 30 job description web and stored them into a list called job_desc where each item is a job description.
# each item is a list of tokenized job_description
tok = [nltk.word_tokenize(job.lower()) for job in job_desc]
# ignore stop words, bullets, etc. And put it into one list
from nltk.corpus import stopwords
stop = stopwords.words('english')
def clean_token(what_to_clean):
cleaned_tok =
for lists in what_to_clean:
for item in lists:
if len(item)>2 and (item not in stop):
cleaned_tok.append(item)
return cleaned_tok
After cleaning job description I've found most frequent words using:
freq = nltk.FreqDist(clean_token(tok))
most_freq_words = freq.most_common(100)
Which outputs:
[('data', 211),
('experience', 78),
('learning', 70),
('business', 65),
('team', 53),
('science', 51),
('machine', 48),.....
From here I only want to extract words like machine, python, C+, technical skills. How can I go about it?
Also you can see there is word "machine" showing up 48 times and I am not sure whether it is talking about machine learning how can I go about this, I know if I want to make predictions I could've used CountVectorizer and n-grams.
python nltk
python nltk
asked 10 mins ago
h_muskh_musk
61
asked 10 mins ago
h_muskh_musk
61
asked 10 mins ago
h_muskh_musk
61
asked 10 mins ago
h_muskh_musk
61
asked 10 mins ago
h_muskh_musk
61
61
add a comment |
add a comment |
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