如何使用sklearn库使用朴素贝叶斯执行文本分类？

Question

I am trying text classification using naive bayes text classifier. My data is in the below format and based on the question and excerpt i have to decide the topic of the question. The training data is having more than 20K records. I know SVM would be a better option here but i want to go with Naive Bayes using sklearn library .

{[{"topic":"electronics","question":"What is the effective differencial effective of this circuit","excerpt":"I'm trying to work out, in general terms, the effective capacitance of this circuit (see diagram: http://i.stack.imgur.com/BS85b.png).  \n\nWhat is the effective capacitance of this circuit and will the ...\r\n        "},
{"topic":"electronics","question":"Outlet Installation--more wires than my new outlet can use [on hold]","excerpt":"I am replacing a wall outlet with a Cooper Wiring USB outlet (TR7745).  The new outlet has 3 wires coming out of it--a black, a white, and a green.  Each one needs to be attached with a wire nut to ...\r\n        "}]}

This is what i have tried so far,

import numpy as np
import json
from sklearn.naive_bayes import *

topic = []
question = []
excerpt = []

with open('training.json') as f:
    for line in f:
        data = json.loads(line)
        topic.append(data["topic"])
        question.append(data["question"])
        excerpt.append(data["excerpt"])

unique_topics = list(set(topic))
new_topic = [x.encode('UTF8') for x in topic]
numeric_topics = [name.replace('gis', '1').replace('security', '2').replace('photo', '3').replace('mathematica', '4').replace('unix', '5').replace('wordpress', '6').replace('scifi', '7').replace('electronics', '8').replace('android', '9').replace('apple', '10') for name in new_topic]
numeric_topics = [float(i) for i in numeric_topics]

x1 = np.array(question)
x2 = np.array(excerpt)
X = zip(*[x1,x2])
Y = np.array(numeric_topics)
print X[0]
clf = BernoulliNB()
clf.fit(X, Y)
print "Prediction:", clf.predict( ['hello'] )

But as expected i am getting ValueError: could not convert string to float. My question is how can i create a simple classifier to classify the question and excerpt into related topic ?

Answer 1

All classifiers in sklearn require input to be represented as vectors of some fixed dimensionality. For text there are CountVectorizer , HashingVectorizer and TfidfVectorizer which can transform your strings into vectors of floating numbers.

vect = TfidfVectorizer()
X = vect.fit_transform(X)

Obviously, you'll need to vectorize your test set in the same way

clf.predict( vect.transform(['hello']) )

See a tutorial on using sklearn with textual data .