private/NlpInPracticeCourse/OpinionSentiment: classify.py

#! /usr/bin/env python
# -*- coding: utf-8 -*-

import random
from nltk.tokenize import RegexpTokenizer
import csv
import string
import re
from nltk import NaiveBayesClassifier
from nltk import classify

regex = re.compile('[%s]' % re.escape(string.punctuation))

def document_features(document):
        tokens = tokenize(document)
        features = dict([('contains-word(%s)' % w, True) for w in tokens])
        return features

def tokenize(text):
   text = regex.sub(' ', text)
   return tokenizer.tokenize(text.lower())

def get_data(filename):
  labeled_text = []
  with open(filename) as csvfile:
        csvreader = csv.reader(csvfile, delimiter=';')
        for row in csvreader:
            if len(row)<8:
                continue
            good = (row[1].decode("utf-8"), "good")
            bad = (row[2].decode("utf-8"), "bad")
            if good and not bad:
                labeled_text.append(good)
            if bad and not good:
                labeled_text.append(bad)
            if good and bad:
                if row[3]=='100' or row[4]=='100' or row[5]=='100' or row[6]=='100' or row[7]=='100':
                        labeled_text.append(good)
                elif row[3]=='10' or row[4]=='10' or row[5]=='10' or row[6]=='10' or row[7]=='10':
                        labeled_text.append(bad)
                else:
                        pass
#                       print "I dont know whether this comment is good or bad"
  return labeled_text

tokenizer = RegexpTokenizer('\w+|\$[\d\.]+|\S+')
# error in NLTK 3.1
tokenizer._regexp = re.compile('\w+|\$[\d\.]+|\S+', re.UNICODE)
labeled_texts = get_data("reviews.csv")

#print "labeled texts"
#print labeled_texts[:4]

#random.shuffle(labeled_texts)
#print "shuffled labeled texts"
#print labeled_texts[:4]

featurized_set = [(document_features(text), label) for (text, label) in labeled_texts]

s = len(featurized_set)

print "complete set length: {}".format(s)
training_set = featurized_set[:int(s*0.75)]
test_set = featurized_set[int(s*0.25):]

classifier = NaiveBayesClassifier.train(training_set)

print classifier.show_most_informative_features(20)
#print "train set accuracy: {}".format(classify.accuracy(classifier, training_set))
print "test set accuracy: {}".format(classify.accuracy(classifier, test_set))

test_sentence = u'všechno v pořádku, určitě doporučuji'
print test_sentence
print classifier.classify(document_features(test_sentence))
#print classifier.prob_classify(document_features(test_sentence)).prob('good')
#print classifier.prob_classify(document_features(test_sentence)).prob('bad')

test_sentence = u'tenhle obchod opravdu nedoporučuji'
print test_sentence
print classifier.classify(document_features(test_sentence))
#print classifier.prob_classify(document_features(test_sentence)).prob('good')
#print classifier.prob_classify(document_features(test_sentence)).prob('bad')

test_sentence = u'hrozný kšeft, nechoďte tam :-('
print test_sentence
print classifier.classify(document_features(test_sentence))
#print classifier.prob_classify(document_features(test_sentence)).prob('good')
#print classifier.prob_classify(document_features(test_sentence)).prob('bad')