【Python NLTK】自然語言處理利器，打造人工智慧對話系統

import nltk
from nltk.corpus import stopWords
from nltk.tokenize import word_tokenize
from nltk.stem import PorterStemmer

text = "Hello, how are you? I am doing great."
text = text.lower()
text = "".join([ch for ch in text if ch.isalnum() or ch.isspace()])
stop_words = set(stopwords.words("english"))
text = " ".join([word for word in word_tokenize(text) if word not in stop_words])
stemmer = PorterStemmer()
text = " ".join([stemmer.stem(word) for word in word_tokenize(text)])

from nltk.classify import NaiveBayesClassifier
from nltk.corpus import movie_reviews

classified_reviews = [(cateGory, text) for category in movie_reviews.categories()
for fileid in movie_reviews.fileids(category)
for text in movie_reviews.words(fileid)]
feature_extractor = lambda review: {word: True for word in review if word in feature_set}
feature_set = set([word for (category, review) in classified_reviews
 for word in review if word not in stop_words])
train_set, test_set = classified_reviews[50:], classified_reviews[:50]
classifier = NaiveBayesClassifier.train(train_set, feature_extractor)

user_input = "I am looking for a good movie to watch."
features = feature_extractor(user_input)
category = classifier.classify(features)
print(category)