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Document Classification using
the Natural Language Toolkit
Ben Healey
http://benhealey.info
@BenHealey
Source: IStockPhoto
http://upload.wikimedia.org/wikipedia/commons/b/b6/FileStack_retouched.jpg
The
Need for
Automation
http://upload.wikimedia.org/wikipedia/commons/d/d6/Cat_loves_sweets.jpg
Take ur
pick!
Class:
Features:
- # Words
- % ALLCAPS
- Unigrams
- Sender
- And so on.
The Development Set
Classification
Algo.
New Document
(Class Unknown)
Document
Features
Trained Classifier
(Model)
Classified
Document.
Relevant NLTK Modules
• Feature Extraction
–
–
–
–
–
from
from
from
from
from
nltk.corpus import words, stopwords
nltk.stem import PorterStemmer
nltk.tokenize import WordPunctTokenizer
nltk.collocations import BigramCollocationFinder
nltk.metrics import BigramAssocMeasures
– See http://text-processing.com/demo/ for examples
• Machine Learning Algos and Tools
–
–
–
–
from
from
from
from
nltk.classify
nltk.classify
nltk.classify
nltk.classify
import
import
import
import
NaiveBayesClassifier
DecisionTreeClassifier
MaxentClassifier
WekaClassifier
– from nltk.classify.util import accuracy
NaiveBayesClassifier
P(label) ∗ P(features|label)
P(label|features) =
P(features)
P(label) ∗ P(f1|label)∗...∗ P(fn|label)
P(label|features) =
P(features)
http://61.153.44.88/nltk/0.9.5/api/nltk.classify.naivebayes-module.html
http://www.educationnews.org/commentaries/opinions_on_education/91117.html
517,431 Emails
Source: IStockPhoto
Prep: Extract and Load
• Sample* of 20,581 plaintext files
• import MySQLdb, os, random, string
•  MySQL via Python ODBC interface
• File, string manipulation
• Key fields separated out
– To, From, CC, Subject, Body
* Folders for 7 users with a large number of email.
So not representative!
Prep: Extract and Load
• Allocation of random number
• Some feature extraction
– #To, #CCd, #Words, %digits, %CAPS
• Note: more cleaning could be done
• Code at benhealey.info
From: [email protected]
To: [email protected]
Subject: Re: Agenda for FERC Meeting RE: EOL
Louise -We had decided that not having Mark in the room gave us the ability
to wiggle if questions on CFTC vs. FERC regulation arose. As you can
imagine, FERC is starting to grapple with the issue that financial trades
in energy commodities is regulated under the CEA, not the Federal
Power Act or the Natural Gas Act.
Thanks,
Jim
From: [email protected]
To: [email protected]
Subject: Start Date: 1/11/02; HourAhead hour: 5;
Start Date: 1/11/02; HourAhead hour: 5; No ancillary schedules
awarded. No variances detected.
LOG MESSAGES:
PARSING FILE -->> O:\Portland\WestDesk\California
Scheduling\ISO Final Schedules\2002011105.txt
Class[es] assigned for 1,000
randomly selected messages:
External Relations
45
Social/Personal
68
Human Resources
134
Other/Unclear
141
Admin/Planning
158
Info Tech
167
Regulatory/Accounting
172
Deals, Trading, Modelling
247
0
50
100
150
200
250
Prep: Show us ur Features
• NLTK toolset
–
–
–
–
–
from
from
from
from
from
nltk.corpus import words, stopwords
nltk.stem import PorterStemmer
nltk.tokenize import WordPunctTokenizer
nltk.collocations import BigramCollocationFinder
nltk.metrics import BigramAssocMeasures
• Custom code
– def extract_features(record,stemmer,stopset,tokenizer):
…
• Code at benhealey.info
Prep: Show us ur Features
• Features in boolean or nominal form
if record['num_words_in_body']<=20:
features['message_length']='Very Short'
elif record['num_words_in_body']<=80:
features['message_length']='Short'
elif record['num_words_in_body']<=300:
features['message_length']='Medium'
else:
features['message_length']='Long'
Prep: Show us ur Features
• Features in boolean or nominal form
text=record['msg_subject']+" "+record['msg_body']
tokens = tokenizer.tokenize(text)
words = [stemmer.stem(x.lower()) for x in tokens if
x not in stopset and len(x) > 1]
for word in words:
features[word]=True
Sit. Say. Heel.
random.shuffle(dev_set)
cutoff = len(dev_set)*2/3
train_set=dev_set[:cutoff]
test_set=dev_set[cutoff:]
classifier =
NaiveBayesClassifier.train(train_set)
print 'accuracy for > ',subject,':',
accuracy(classifier, test_set)
classifier.show_most_informative_features(10)
Most Important Features
Most Important Features
Most Important Features
Performance: ‘IT’ Model
Decile Mean Prob.
9
1.0000
8
0.7364
7
0.0000
6
0.0000
5
0.0000
4
0.0000
3
0.0000
2
0.0000
1
0.0000
0
0.0000
% PR
3
4
1
5
4
6
7
15
% Social
1
11
11
8
7
6
7
8
4
5
IMPORTANT: These are ‘cheat’ scores!
% HR
7
10
14
11
16
16
18
13
29
% Other % Admin
1
17
13
30
21
13
16
13
18
16
17
19
28
10
16
12
14
11
14
% IT
95
49
2
2
4
4
6
2
2
2
% Legal
2
5
13
11
13
17
16
37
42
16
% Deal
3
13
25
38
41
32
25
21
21
28
Performance: ‘Deal’ Model
Decile Mean Prob.
9
1.0000
8
1.0000
7
0.9971
6
0.1680
5
0.0000
4
0.0000
3
0.0000
2
0.0000
1
0.0000
0
0.0000
% PR
2
1
2
3
4
5
5
7
16
% Social
2
3
15
11
8
9
6
4
10
IMPORTANT: These are ‘cheat’ scores!
% HR
2
1
3
9
19
21
21
22
3
33
% Other % Admin
11
6
3
4
18
4
35
17
22
19
17
32
13
25
9
26
2
5
11
20
% IT
5
4
2
11
9
14
13
18
77
14
% Legal
14
3
17
17
23
18
28
24
13
15
% Deal
79
93
58
9
4
2
3
Performance: ‘Social’ Model
Decile Mean Prob.
9
1.0000
8
1.0000
7
1.0000
6
1.0000
5
1.0000
4
1.0000
3
1.0000
2
1.0000
1
0.7382
0
0.0001
% PR
1
7
2
22
10
1
2
-
% Social
9
15
5
2
11
7
15
3
1
-
IMPORTANT: These are ‘cheat’ scores!
% HR
6
17
6
21
32
20
10
15
7
-
% Other % Admin
9
11
18
16
24
20
15
25
9
22
13
22
5
9
22
24
25
9
1
-
% IT
13
2
3
11
10
13
7
14
4
89
% Legal
40
21
30
24
5
10
14
14
13
1
% Deal
24
21
18
22
10
24
49
24
45
10
Don’t get burned.
•
•
•
•
Biased samples
Accuracy and rare events
Features and prior knowledge
Good modelling is iterative!
• Resampling and robustness
• Learning cycles
http://www.ugo.com/movies/mustafa-in-austin-powers
Resources
• NLTK:
– www.nltk.org/
– http://www.nltk.org/book
• Enron email datasets:
– http://www.cs.umass.edu/~ronb/enron_dataset.html
• Free online Machine Learning course from Stanford
– http://ml-class.com/ (starts in October)
• StreamHacker blog by Jacob Perkins
– http://streamhacker.com