class Classifications():
#static variables
_category_path = os.path.join(os.path.dirname(__file__),
"classifiers/category.slp")
_rating_path = os.path.join(os.path.dirname(__file__),
"classifiers/rating.slp")
_rating_nlp_path = os.path.join(os.path.dirname(__file__),
"classifiers/rating_nlp.svm")
_sentiment_path = os.path.join(os.path.dirname(__file__),
"classifiers/sentiment.nb")
_category = SLP.load(_category_path)
_rating = SLP.load(_rating_path)
_rating_nlp = SVM.load(_rating_nlp_path)
_sentiment = NB.load(_sentiment_path)
@staticmethod
def selectWords(review):
'''
a function that gets a review and selects the nouns, adjectives, verbs and exclamation mark
'''
review = parsetree(review, lemmata=True)[0] #lemmatize the review
#select adjectives (JJ), nouns (NN), verbs (VB) and exclamation marks
review = [
w.lemma for w in review
if w.tag.startswith(('JJ', 'NN', 'VB', '!'))
]
review = count(review) #a dictionary of (word, count)
return review
@staticmethod
def classify(text):
predicted_category = Classifications._category.classify(Document(text),
discrete=True)
predicted_rate = Classifications._rating.classify(Document(text),
discrete=True)
predicted_rate_nlp = Classifications._rating_nlp.classify(
Classifications.selectWords(text), discrete=True)
predicted_sentiment_dict = Classifications._sentiment.classify(
Classifications.selectWords(text), discrete=False)
predicted_sentiment = True if str(
sorted(predicted_sentiment_dict.items(),
key=operator.itemgetter(1),
reverse=True)[1][0]) in ['True', '3.0', '4.0', '5.0'
] else False
return {
'text': text,
'rate': predicted_rate,
'category': predicted_category,
'rate_nlp': predicted_rate_nlp,
'positivity': predicted_sentiment
}
from pattern.vector import SVM
#from Jseg import jieba
from os.path import realpath, dirname, join
CUR_PATH = dirname(realpath(__file__))
sentipol_cls = SVM.load(join(CUR_PATH, 'svm_mod.gpk'))
execfile(join(CUR_PATH, 'Sentipol.py'))
def sentipol_tmp(text):
from Jseg import jieba
text = jieba.seg(text).nopos().split()
pol = sentipol_cls.classify(text)
details = sentipol(text)
return pol, details
from pattern.vector import SVM, KNN, NB, count, shuffled
from pattern.en import tag, predicative
classifier = SVM()
classifier = SVM.load("sentiment.p")
def instance(review): # "Great book!"
v = tag(review) # [("Great", "JJ"), ("book", "NN"), ("!", "!")]
v = [word for (word, pos) in v if pos in ("JJ", "RB", "VB", "VBZ", "NN", "NNS", "NNP", "NNPS") or word in ("!")]
v = [predicative(word) for word in v] # ["great", "!", "!"]
v = count(v) # {"great": 1, "!": 1}
return v
score = classifier.classify(instance("you little bitch"))
print(score)
