First, read these answers carefully, they contain parts of the answers you require and also briefly explains what the classifier does and how it works in NLTK:
Testing classifier on annotated data
Now to answer your question. We assume that your question is a follow-up of this question: Using my own corpus instead of movie_reviews corpus for Classification in NLTK
If your test text is structured the same way as the movie_review corpus, then you can simply read the test data as you would for the training data:
Just in case the explanation of the code is unclear, here's a walkthrough:
traindir = '/home/alvas/my_movie_reviews'
mr = CategorizedPlaintextCorpusReader(traindir, r'(?!\.).*\.txt', cat_pattern=r'(neg|pos)/.*', encoding='ascii')
The two lines above is to read a directory my_movie_reviews with such a structure:
\my_movie_reviews
\pos
123.txt
234.txt
\neg
456.txt
789.txt
README
Then the next line extracts documents with its pos/neg tag that's part of the directory structure.
documents = [([w for w in mr.words(i) if w.lower() not in stop and w not in string.punctuation], i.split('/')[0]) for i in mr.fileids()]
Here's the explanation for the above line:
# This extracts the pos/neg tag
labels = [i for i.split('/')[0]) for i in mr.fileids()]
# Reads the words from the corpus through the CategorizedPlaintextCorpusReader object
words = [w for w in mr.words(i)]
# Removes the stopwords
words = [w for w in mr.words(i) if w.lower() not in stop]
# Removes the punctuation
words = [w for w in mr.words(i) w not in string.punctuation]
# Removes the stopwords and punctuations
words = [w for w in mr.words(i) if w.lower() not in stop and w not in string.punctuation]
# Removes the stopwords and punctuations and put them in a tuple with the pos/neg labels
documents = [([w for w in mr.words(i) if w.lower() not in stop and w not in string.punctuation], i.split('/')[0]) for i in mr.fileids()]
The SAME process should be applied when you read the test data!!!
Now to the feature processing:
The following lines extra top 100 features for the classifier:
# Extract the words features and put them into FreqDist
# object which records the no. of times each unique word occurs
word_features = FreqDist(chain(*[i for i,j in documents]))
# Cuts the FreqDist to the top 100 words in terms of their counts.
word_features = word_features.keys()[:100]
Next to processing the documents into classify-able format:
# Splits the training data into training size and testing size
numtrain = int(len(documents) * 90 / 100)
# Process the documents for training data
train_set = [({i:(i in tokens) for i in word_features}, tag) for tokens,tag in documents[:numtrain]]
# Process the documents for testing data
test_set = [({i:(i in tokens) for i in word_features}, tag) for tokens,tag in documents[numtrain:]]
Now to explain that long list comprehension for train_set and `test_set:
# Take the first `numtrain` no. of documents
# as training documents
train_docs = documents[:numtrain]
# Takes the rest of the documents as test documents.
test_docs = documents[numtrain:]
# These extract the feature sets for the classifier
# please look at the full explanation on https://mcmap.net/q/555044/-nltk-naivebayesclassifier-training-for-sentiment-analysis/
train_set = [({i:(i in tokens) for i in word_features}, tag) for tokens,tag in train_docs]
You need to process the documents as above for the feature extractions in the test documents too!!!
So here's how you can read the test data:
stop = stopwords.words('english')
# Reads the training data.
traindir = '/home/alvas/my_movie_reviews'
mr = CategorizedPlaintextCorpusReader(traindir, r'(?!\.).*\.txt', cat_pattern=r'(neg|pos)/.*', encoding='ascii')
# Converts training data into tuples of [(words,label), ...]
documents = [([w for w in mr.words(i) if w.lower() not in stop and w not in string.punctuation], i.split('/')[0]) for i in mr.fileids()]
# Now do the same for the testing data.
testdir = '/home/alvas/test_reviews'
mr_test = CategorizedPlaintextCorpusReader(testdir, r'(?!\.).*\.txt', cat_pattern=r'(neg|pos)/.*', encoding='ascii')
# Converts testing data into tuples of [(words,label), ...]
test_documents = [([w for w in mr_test.words(i) if w.lower() not in stop and w not in string.punctuation], i.split('/')[0]) for i in mr_test.fileids()]
Then continue with the processing steps described above, and simply do this to get the label for the test document as @yvespeirsman answered:
#### FOR TRAINING DATA ####
stop = stopwords.words('english')
# Reads the training data.
traindir = '/home/alvas/my_movie_reviews'
mr = CategorizedPlaintextCorpusReader(traindir, r'(?!\.).*\.txt', cat_pattern=r'(neg|pos)/.*', encoding='ascii')
# Converts training data into tuples of [(words,label), ...]
documents = [([w for w in mr.words(i) if w.lower() not in stop and w not in string.punctuation], i.split('/')[0]) for i in mr.fileids()]
# Extract training features.
word_features = FreqDist(chain(*[i for i,j in documents]))
word_features = word_features.keys()[:100]
# Assuming that you're using full data set
# since your test set is different.
train_set = [({i:(i in tokens) for i in word_features}, tag) for tokens,tag in documents]
#### TRAINS THE TAGGER ####
# Train the tagger
classifier = NaiveBayesClassifier.train(train_set)
#### FOR TESTING DATA ####
# Now do the same reading and processing for the testing data.
testdir = '/home/alvas/test_reviews'
mr_test = CategorizedPlaintextCorpusReader(testdir, r'(?!\.).*\.txt', cat_pattern=r'(neg|pos)/.*', encoding='ascii')
# Converts testing data into tuples of [(words,label), ...]
test_documents = [([w for w in mr_test.words(i) if w.lower() not in stop and w not in string.punctuation], i.split('/')[0]) for i in mr_test.fileids()]
# Reads test data into features:
test_set = [({i:(i in tokens) for i in word_features}, tag) for tokens,tag in test_documents]
#### Evaluate the classifier ####
for doc, gold_label in test_set:
tagged_label = classifier.classify(doc)
if tagged_label == gold_label:
print("Woohoo, correct")
else:
print("Boohoo, wrong")
If the above code and explanation makes no sense to you, then you MUST read this tutorial before proceeding: http://www.nltk.org/howto/classify.html
Now let's say you have no annotation in your test data, i.e. your test.txt is not in the directory structure like the movie_review and just a plain textfile:
\test_movie_reviews
\1.txt
\2.txt
Then there's no point in reading it into a categorized corpus, you can simply do read and tag the documents, i.e.:
for infile in os.listdir(`test_movie_reviews):
for line in open(infile, 'r'):
tagged_label = classifier.classify(doc)
BUT you CANNOT evaluate the results without annotation, so you can't check the tag if the if-else, also you need to tokenize your text if you're not using the CategorizedPlaintextCorpusReader.
If you just want to tag a plaintext file test.txt:
import string
from itertools import chain
from nltk.corpus import stopwords
from nltk.probability import FreqDist
from nltk.classify import NaiveBayesClassifier
from nltk.corpus import movie_reviews
from nltk import word_tokenize
stop = stopwords.words('english')
# Extracts the documents.
documents = [([w for w in movie_reviews.words(i) if w.lower() not in stop and w.lower() not in string.punctuation], i.split('/')[0]) for i in movie_reviews.fileids()]
# Extract the features.
word_features = FreqDist(chain(*[i for i,j in documents]))
word_features = word_features.keys()[:100]
# Converts documents to features.
train_set = [({i:(i in tokens) for i in word_features}, tag) for tokens,tag in documents]
# Train the classifier.
classifier = NaiveBayesClassifier.train(train_set)
# Tag the test file.
with open('test.txt', 'r') as fin:
for test_sentence in fin:
# Tokenize the line.
doc = word_tokenize(test_sentence.lower())
featurized_doc = {i:(i in doc) for i in word_features}
tagged_label = classifier.classify(featurized_doc)
print(tagged_label)
Once again, please don't just copy and paste the solution and try to understand why and how it works.
posbut the program showneg. And I don't know the reason. – Leroi