As I am new to programming, I wish to know that is it possible to use the nltk built-in movie review dataset to do sentiment analysis by using KNN to determine the polarity of data? Is there any way to do so?
import nltk
from nltk.corpus import movie_reviews
from nltk.tokenize import word_tokenize
documents = [(list(movie_reviews.words(fileid)), category)
for category in movie_reviews.categories()
for fileid in movie_reviews.fileids(category)]
all_words = []
for w in movie_reviews.words():
all_words.append(w.lower())
all_words = nltk.FreqDist(all_words)
word_features = list(all_words.keys())[:3000]
def find_features(document):
words = set(document)
features = {}
for w in word_features:
features[w] = (w in words)
return features
featuresets = [(find_features(rev), category) for (rev, category) in documents]
training_set = featuresets[500:1500]
testing_set = featuresets[:1500]
classifier = nltk.DecisionTreeClassifier.train(training_set)
print "Classifier accuracy percent:",(nltk.classify.accuracy(classifier, testing_set))*100 , "%"
string = raw_input("Enter the string: ")
print (classifier.classify(find_features(word_tokenize(string))))
As I am trying to convert the code above from decision-tree to KNN
