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import pandas as pd
import numpy as np
import pickle
from sklearn.feature_extraction.text import CountVectorizer
from sklearn.feature_extraction.text import TfidfTransformer
from sklearn.pipeline import Pipeline
from nltk.stem.snowball import SnowballStemmer
from sklearn.linear_model import SGDClassifier
from sklearn.naive_bayes import MultinomialNB
class Models:
def __init__(self):
self.name = ''
path = 'dataset/trainingdata.csv'
df = pd.read_csv(path)
df = df.dropna()
self.x = df['sentences']
self.y = df['sentiments']
def mnb_classifier(self):
self.name = 'MultinomialNB classifier'
classifier = Pipeline([('vect', CountVectorizer()), ('tfidf', TfidfTransformer()), ('clf', MultinomialNB())])
return classifier.fit(self.x, self.y)
def svm_classifier(self):
self.name = 'SVM classifier'
classifier = Pipeline([('vect', CountVectorizer()), ('tfidf', TfidfTransformer()),('clf-svm', SGDClassifier(loss='hinge', penalty='l2',alpha=1e-3, random_state=42))])
classifier = classifier.fit(self.x, self.y)
pickle.dump(classifier,open(self.name + '.pkl', "wb"))
return classifier
def mnb_stemmed_classifier(self):
self.name = 'MultinomialNB stemmed classifier'
self.stemmed_count_vect = StemmedCountVectorizer(stop_words='english')
classifier = Pipeline([('vect', self.stemmed_count_vect), ('tfidf', TfidfTransformer()),('mnb', MultinomialNB(fit_prior=False))])
classifier = classifier.fit(self.x, self.y)
pickle.dump(classifier, open(self.name + '.pkl', "wb"))
return classifier
def svm_stemmed_classifier(self):
self.name = 'SVM stemmed classifier'
self.stemmed_count_vect = StemmedCountVectorizer(stop_words='english')
classifier = Pipeline([('vect', self.stemmed_count_vect), ('tfidf', TfidfTransformer()),('clf-svm', SGDClassifier())])
classifier = classifier.fit(self.x, self.y)
pickle.dump(classifier, open(self.name + '.pkl', "wb"))
return classifier
def accuracy(self, model):
predicted = model.predict(self.x)
accuracy = np.mean(predicted == self.y)
print(f"{self.name} has accuracy of {accuracy * 100} % ")
class StemmedCountVectorizer(CountVectorizer):
def build_analyzer(self):
stemmer = SnowballStemmer("english", ignore_stopwords=True)
analyzer = super(StemmedCountVectorizer, self).build_analyzer()
return lambda doc: ([stemmer.stem(w) for w in analyzer(doc)])
if __name__ == '__main__':
model = Models()
model.accuracy(model.mnb_classifier())
model.accuracy(model.svm_classifier())
model.accuracy(model.mnb_stemmed_classifier())
model.accuracy(model.svm_stemmed_classifier())
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