genderizer3 0.1.2.6

Genderizer tries to infer gender information looking at first name and/or making text analysis

Genderizer

Genderizer is a language independent module which tries to detect gender by looking at given first names and/or analyzing sample texts.

Always remember:

Data-driven predictions can succeed --and they can fail. It is when we deny our role in the process that the odds of failure rise. Before we demand more of our data, we need to demand more of ourselves. -- The Signal and The Noise by Nate Silver

##Installation You can install this package using the following pip command:

$ sudo pip install genderizer3

##Examples

from genderizer3.genderizer3 import Genderizer

print(Genderizer.detect(firstName='John'))
# >>> male

print(Genderizer.detect(firstName='Marry'))
# >>> female

print(Genderizer.detect(firstName='mustafa'))
# >>> male

print(Genderizer.detect(firstName='fatma'))
# >>> female

print(Genderizer.detect(firstName='fikret'))
# >>> None

print(Genderizer.detect(firstName='fikret', text='galatasary maçı kaçmaz'))
# >>> male

print(Genderizer.detect(firstName='fikret', text='annemlerle yemek yedik'))
# >>> female

print(Genderizer.detect(text='askerlik yoklamasını kaçırdım mk'))
# >>> male

print(Genderizer.detect(text='bana çiçek alan erkek için canım feda'))
# >>> female

print(Genderizer.detect(firstName='fatma', text='askerlik yoklamasını kaçırdım mk'))
# >>> female

print(Genderizer.detect(text='futbol sevgi'))
# >>> None

print(Genderizer.detect(text='lan bi siktir git'))
# >>> male

Note: You may claim that women can say lan bi siktir git, of course. But the probability of being female is less than the probability of being male according to the trained data of the classifier.

So it is obvious that the success of detection depends on the trained data.

By the way, in Turkish saying 'lan bi siktir git' makes you quite rude.

How It Works

Genderizer is a module which tries to detect gender by looking given first names and/or analyzing sample text of a person.

If a first name is definitely used for only one gender, the system will accept this gender and will not make any further analysis. For example, while 'Mustafa', 'Osman', 'Hasan' is used in Turkish only for male; 'Fatma', 'Ceyda', 'Elif' only for female.

When looking at first names does not infer any gender for sure, the system will make text analysis if it is given. For example; 'Ekim', 'Meric', or 'Tümay' is used for both male and female.

The text analysis is the classification of sample texts. It simply try to compute the probability of being male or female mining the sample text. In this system Naive Bayesian Classification is adopted and naiveBayesClassifier is used.

How To Improve It

TODO: write a step by step guide

##Preparing Language Dependent Training Sets TODO: give a few examples

Customization and Optimization

Using Memcached For Speed

"""
Under heavy usage, for example tens of thousands detection request
in a few seconds the default configuration could not meet the
demand. By the default configurations, genderize will load necessary
data from files and this is well known to be slow. Instead of each
time loading data into memory, doing this one time will be clever
approach. One of the best way of this approach is to use memcached.
For more information have a look at the documentation of memcached.

Genderizer provides a memcached interface to store first names in 
memory. To active this interface, you need to instantiate 
memcachedNamesCollection interface and pass it to genderizer3 while 
initializing it.
"""

from genderizer3.memcachedNamesCollection import MemcachedNamesCollection

# For memcached, do not forget to setup the memcached server.
MemcachedNamesCollection.memcacheHost = '127.0.0.1:11211'
Genderizer.init(
    namesCollection=MemcachedNamesCollection
)
print
Genderizer.detect(firstName='John')

Using Mongodb

"""
If you want to use Genderize on Mongodb for arbitrary reasons, the
MongoNamesCollection first names collection interface will do much
of the necessary works for you.
"""
from genderizer3.mongoNamesCollection import MongoNamesCollection

MongoNamesCollection.mongodbURL = 'mongodb://192.168.1.170'
Genderizer.init(
    namesCollection=MongoNamesCollection
)
print
Genderizer.detect(firstName='Marry')

Custom Text Classifier

"""
NaiveBayesClassifier is adopted as the default classifier. But you
can use another, entirely different classifier; as long as the
classifier has a 'classify' method taking text as a parameter.

For more information please have a look at the naiveBayesClassifier
project's documentation.
https://github.com/muatik/naive-bayes-classifier
"""

from naiveBayesClassifier import tokenizer
from naiveBayesClassifier.classifier import Classifier
from cachedModel import CachedModel

Genderizer.init(
    lang='en',
    classifier=Classifier(CachedModel.get('en'), tokenizer)
)

print
Genderizer.detect(firstName='fikret', text='annemle kahve keyfi')

TODO

• inline docs
• unit-tests

Original AUTHORS

• Mustafa Atik @muatik
• Nejdet Yucesoy @nejdetckenobi