nltk NaiveBayesClassifier training for sentiment analysis
You need to change your data structure. Here is your train list as it currently stands:
>>> train = [('I love this sandwich.', 'pos'),
('This is an amazing place!', 'pos'),
('I feel very good about these beers.', 'pos'),
('This is my best work.', 'pos'),
("What an awesome view", 'pos'),
('I do not like this restaurant', 'neg'),
('I am tired of this stuff.', 'neg'),
("I can't deal with this", 'neg'),
('He is my sworn enemy!', 'neg'),
('My boss is horrible.', 'neg')]
The problem is, though, that the first element of each tuple should be a dictionary of features. So I will change your list into a data structure that the classifier can work with:
>>> from nltk.tokenize import word_tokenize # or use some other tokenizer
>>> all_words = set(word.lower() for passage in train for word in word_tokenize(passage[0]))
>>> t = [({word: (word in word_tokenize(x[0])) for word in all_words}, x[1]) for x in train]
Your data should now be structured like this:
>>> t
[({'this': True, 'love': True, 'deal': False, 'tired': False, 'feel': False, 'is': False, 'am': False, 'an': False, 'sandwich': True, 'ca': False, 'best': False, '!': False, 'what': False, '.': True, 'amazing': False, 'horrible': False, 'sworn': False, 'awesome': False, 'do': False, 'good': False, 'very': False, 'boss': False, 'beers': False, 'not': False, 'with': False, 'he': False, 'enemy': False, 'about': False, 'like': False, 'restaurant': False, 'these': False, 'of': False, 'work': False, "n't": False, 'i': False, 'stuff': False, 'place': False, 'my': False, 'view': False}, 'pos'), . . .]
Note that the first element of each tuple is now a dictionary. Now that your data is in place and the first element of each tuple is a dictionary, you can train the classifier like so:
>>> import nltk
>>> classifier = nltk.NaiveBayesClassifier.train(t)
>>> classifier.show_most_informative_features()
Most Informative Features
this = True neg : pos = 2.3 : 1.0
this = False pos : neg = 1.8 : 1.0
an = False neg : pos = 1.6 : 1.0
. = True pos : neg = 1.4 : 1.0
. = False neg : pos = 1.4 : 1.0
awesome = False neg : pos = 1.2 : 1.0
of = False pos : neg = 1.2 : 1.0
feel = False neg : pos = 1.2 : 1.0
place = False neg : pos = 1.2 : 1.0
horrible = False pos : neg = 1.2 : 1.0
If you want to use the classifier, you can do it like this. First, you begin with a test sentence:
>>> test_sentence = "This is the best band I've ever heard!"
Then, you tokenize the sentence and figure out which words the sentence shares with all_words. These constitute the sentence's features.
>>> test_sent_features = {word: (word in word_tokenize(test_sentence.lower())) for word in all_words}
Your features will now look like this:
>>> test_sent_features
{'love': False, 'deal': False, 'tired': False, 'feel': False, 'is': True, 'am': False, 'an': False, 'sandwich': False, 'ca': False, 'best': True, '!': True, 'what': False, 'i': True, '.': False, 'amazing': False, 'horrible': False, 'sworn': False, 'awesome': False, 'do': False, 'good': False, 'very': False, 'boss': False, 'beers': False, 'not': False, 'with': False, 'he': False, 'enemy': False, 'about': False, 'like': False, 'restaurant': False, 'this': True, 'of': False, 'work': False, "n't": False, 'these': False, 'stuff': False, 'place': False, 'my': False, 'view': False}
Then you simply classify those features:
>>> classifier.classify(test_sent_features)
'pos' # note 'best' == True in the sentence features above
This test sentence appears to be positive.
@275365's tutorial on the data structure for NLTK's bayesian classifier is great. From a more high level, we can look at it as,
We have inputs sentences with sentiment tags:
training_data = [('I love this sandwich.', 'pos'),
('This is an amazing place!', 'pos'),
('I feel very good about these beers.', 'pos'),
('This is my best work.', 'pos'),
("What an awesome view", 'pos'),
('I do not like this restaurant', 'neg'),
('I am tired of this stuff.', 'neg'),
("I can't deal with this", 'neg'),
('He is my sworn enemy!', 'neg'),
('My boss is horrible.', 'neg')]
Let's consider our feature sets to be individual words, so we extract a list of all possible words from the training data (let's call it vocabulary) as such:
from nltk.tokenize import word_tokenize
from itertools import chain
vocabulary = set(chain(*[word_tokenize(i[0].lower()) for i in training_data]))
Essentially, vocabulary here is the same @275365's all_word
>>> all_words = set(word.lower() for passage in training_data for word in word_tokenize(passage[0]))
>>> vocabulary = set(chain(*[word_tokenize(i[0].lower()) for i in training_data]))
>>> print vocabulary == all_words
True
From each data point, (i.e. each sentence and the pos/neg tag), we want to say whether a feature (i.e. a word from the vocabulary) exist or not.
>>> sentence = word_tokenize('I love this sandwich.'.lower())
>>> print {i:True for i in vocabulary if i in sentence}
{'this': True, 'i': True, 'sandwich': True, 'love': True, '.': True}
But we also want to tell the classifier which word don't exist in the sentence but in the vocabulary, so for each data point, we list out all possible words in the vocabulary and say whether a word exist or not:
>>> sentence = word_tokenize('I love this sandwich.'.lower())
>>> x = {i:True for i in vocabulary if i in sentence}
>>> y = {i:False for i in vocabulary if i not in sentence}
>>> x.update(y)
>>> print x
{'love': True, 'deal': False, 'tired': False, 'feel': False, 'is': False, 'am': False, 'an': False, 'good': False, 'best': False, '!': False, 'these': False, 'what': False, '.': True, 'amazing': False, 'horrible': False, 'sworn': False, 'ca': False, 'do': False, 'sandwich': True, 'very': False, 'boss': False, 'beers': False, 'not': False, 'with': False, 'he': False, 'enemy': False, 'about': False, 'like': False, 'restaurant': False, 'this': True, 'of': False, 'work': False, "n't": False, 'i': True, 'stuff': False, 'place': False, 'my': False, 'awesome': False, 'view': False}
But since this loops through the vocabulary twice, it's more efficient to do this:
>>> sentence = word_tokenize('I love this sandwich.'.lower())
>>> x = {i:(i in sentence) for i in vocabulary}
{'love': True, 'deal': False, 'tired': False, 'feel': False, 'is': False, 'am': False, 'an': False, 'good': False, 'best': False, '!': False, 'these': False, 'what': False, '.': True, 'amazing': False, 'horrible': False, 'sworn': False, 'ca': False, 'do': False, 'sandwich': True, 'very': False, 'boss': False, 'beers': False, 'not': False, 'with': False, 'he': False, 'enemy': False, 'about': False, 'like': False, 'restaurant': False, 'this': True, 'of': False, 'work': False, "n't": False, 'i': True, 'stuff': False, 'place': False, 'my': False, 'awesome': False, 'view': False}
So for each sentence, we want to tell the classifier for each sentence which word exist and which word doesn't and also give it the pos/neg tag. We can call that a feature_set, it's a tuple made up of a x (as shown above) and its tag.
>>> feature_set = [({i:(i in word_tokenize(sentence.lower())) for i in vocabulary},tag) for sentence, tag in training_data]
[({'this': True, 'love': True, 'deal': False, 'tired': False, 'feel': False, 'is': False, 'am': False, 'an': False, 'sandwich': True, 'ca': False, 'best': False, '!': False, 'what': False, '.': True, 'amazing': False, 'horrible': False, 'sworn': False, 'awesome': False, 'do': False, 'good': False, 'very': False, 'boss': False, 'beers': False, 'not': False, 'with': False, 'he': False, 'enemy': False, 'about': False, 'like': False, 'restaurant': False, 'these': False, 'of': False, 'work': False, "n't": False, 'i': False, 'stuff': False, 'place': False, 'my': False, 'view': False}, 'pos'), ...]
Then we feed these features and tags in the feature_set into the classifier to train it:
from nltk import NaiveBayesClassifier as nbc
classifier = nbc.train(feature_set)
Now you have a trained classifier and when you want to tag a new sentence, you have to "featurize" the new sentence to see which of the word in the new sentence are in the vocabulary that the classifier was trained on:
>>> test_sentence = "This is the best band I've ever heard! foobar"
>>> featurized_test_sentence = {i:(i in word_tokenize(test_sentence.lower())) for i in vocabulary}
NOTE: As you can see from the step above, the naive bayes classifier cannot handle out of vocabulary words since the foobar token disappears after you featurize it.
Then you feed the featurized test sentence into the classifier and ask it to classify:
>>> classifier.classify(featurized_test_sentence)
'pos'
Hopefully this gives a clearer picture of how to feed data in to NLTK's naive bayes classifier for sentimental analysis. Here's the full code without the comments and the walkthrough:
from nltk import NaiveBayesClassifier as nbc
from nltk.tokenize import word_tokenize
from itertools import chain
training_data = [('I love this sandwich.', 'pos'),
('This is an amazing place!', 'pos'),
('I feel very good about these beers.', 'pos'),
('This is my best work.', 'pos'),
("What an awesome view", 'pos'),
('I do not like this restaurant', 'neg'),
('I am tired of this stuff.', 'neg'),
("I can't deal with this", 'neg'),
('He is my sworn enemy!', 'neg'),
('My boss is horrible.', 'neg')]
vocabulary = set(chain(*[word_tokenize(i[0].lower()) for i in training_data]))
feature_set = [({i:(i in word_tokenize(sentence.lower())) for i in vocabulary},tag) for sentence, tag in training_data]
classifier = nbc.train(feature_set)
test_sentence = "This is the best band I've ever heard!"
featurized_test_sentence = {i:(i in word_tokenize(test_sentence.lower())) for i in vocabulary}
print "test_sent:",test_sentence
print "tag:",classifier.classify(featurized_test_sentence)
It appears that you are trying to use TextBlob but are training the NLTK NaiveBayesClassifier, which, as pointed out in other answers, must be passed a dictionary of features.
TextBlob has a default feature extractor that indicates which words in the training set are included in the document (as demonstrated in the other answers). Therefore, TextBlob allows you to pass in your data as is.
from textblob.classifiers import NaiveBayesClassifier
train = [('This is an amazing place!', 'pos'),
('I feel very good about these beers.', 'pos'),
('This is my best work.', 'pos'),
("What an awesome view", 'pos'),
('I do not like this restaurant', 'neg'),
('I am tired of this stuff.', 'neg'),
("I can't deal with this", 'neg'),
('He is my sworn enemy!', 'neg'),
('My boss is horrible.', 'neg') ]
test = [
('The beer was good.', 'pos'),
('I do not enjoy my job', 'neg'),
("I ain't feeling dandy today.", 'neg'),
("I feel amazing!", 'pos'),
('Gary is a friend of mine.', 'pos'),
("I can't believe I'm doing this.", 'neg') ]
classifier = NaiveBayesClassifier(train) # Pass in data as is
# When classifying text, features are extracted automatically
classifier.classify("This is an amazing library!") # => 'pos'
Of course, the simple default extractor is not appropriate for all problems. If you would like to how features are extracted, you just write a function that takes a string of text as input and outputs the dictionary of features and pass that to the classifier.
classifier = NaiveBayesClassifier(train, feature_extractor=my_extractor_func)
I encourage you to check out the short TextBlob classifier tutorial here: http://textblob.readthedocs.org/en/latest/classifiers.html