52 lines
1.2 KiB
Python
52 lines
1.2 KiB
Python
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import numpy as np
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import pandas as pd
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import matplotlib.pyplot as plt
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dataset = pd.read_csv("Restaurant_Reviews.tsv", delimiter="\t", quoting=3)
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import re
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# import nltk
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from nltk.corpus import stopwords
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from nltk.stem.porter import PorterStemmer
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# nltk.download('stopwords')
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ps = PorterStemmer()
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corpus = []
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for i in range(0, 1000):
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review = re.sub('[^a-zA-Z]', ' ', dataset['Review'][i])
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review = review.lower()
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review = review.split()
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review = [ps.stem(word) for word in review if not word in set(stopwords.words('english'))]
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review = ' '.join(review)
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corpus.append(review)
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from sklearn.feature_extraction.text import CountVectorizer
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cv = CountVectorizer(max_features=1500)
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X = cv.fit_transform(corpus).toarray()
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Y = dataset.iloc[:, 1].values
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from sklearn.model_selection import train_test_split
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X_train, X_test, Y_train, Y_test = train_test_split(X, Y, test_size = 0.20, random_state = 0)
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from sklearn.naive_bayes import GaussianNB
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classifier = GaussianNB()
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classifier.fit(X_train, Y_train)
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Y_pred = classifier.predict(X_test)
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print(Y_pred)
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print(Y_test)
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count = 0
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for i in range(0,len(Y_pred)):
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if Y_pred[i] == Y_test[i]:
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count += 1
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print(count / len(Y_pred))
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