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from nltk.tokenize import word_tokenize
from nltk.stem import WordNetLemmatizer
from nltk.corpus import stopwords
from sklearn.metrics import accuracy_score
from sklearn.feature_extraction.text import TfidfVectorizer
from sklearn.model_selection import train_test_split
from imblearn.over_sampling import SMOTE
from sklearn.naive_bayes import MultinomialNB
import nltk
import pandas as pd
lemmatizer = WordNetLemmatizer()
nltk.download('stopwords')
nltk.download('punkt_tab')
nltk.download('all-corpora')
stop_words = set(stopwords.words('english'))
df = pd.read_csv("amazon_reviews.csv")
# Preprocess text data
def preprocess(review):
review = review.lower()
tokens = word_tokenize(review)
lemmas = [lemmatizer.lemmatize(token) for token in tokens if token not in stop_words]
return " ".join(lemmas)
# Format csv data into array of [review, rating]
review_ratings = []
for i in range(len(df)):
review_text = str(df.loc[i]["reviewText"])
rating = int(df.loc[i]["overall"])
review_ratings.append([review_text, rating])
# Create corpus of preprocessed text
corpus = []
for i in range(len(review_ratings)):
review = review_ratings[i][0]
rating = review_ratings[i][1]
preprocessed_text = preprocess(review)
corpus.append(preprocessed_text)
# Convert to vector representation
vectorizer = TfidfVectorizer(max_features=10000)
X = vectorizer.fit_transform(corpus).toarray()
y = [r[1] for r in review_ratings]
# Generate synthetic samples as 5 star rating reviews are overbalanced
smote = SMOTE(random_state=42)
X_resampled, y_resampled = smote.fit_resample(X, y)
X_train, X_test, y_train, y_test = train_test_split(X_resampled, y_resampled, test_size=0.2, random_state=42)
# Create model and fit
model = MultinomialNB()
model.fit(X_train, y_train)
y_predict = model.predict(X_test)
print("Accuracy", accuracy_score(y_test, y_predict))
def predict_rating(review):
preprocessed_text = preprocess(review)
vectorized = vectorizer.transform([preprocessed_text]).toarray()
return model.predict(vectorized) |