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Naive Bayes

The document outlines a Python script that implements a sentiment analysis model using the Naive Bayes algorithm on movie reviews. It utilizes the NLTK library to load movie review data, preprocesses the text, and splits it into training and testing sets. The model is trained and evaluated, providing predictions on new reviews and displaying accuracy and classification metrics.

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Gaurang Rane
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27 views1 page

Naive Bayes

The document outlines a Python script that implements a sentiment analysis model using the Naive Bayes algorithm on movie reviews. It utilizes the NLTK library to load movie review data, preprocesses the text, and splits it into training and testing sets. The model is trained and evaluated, providing predictions on new reviews and displaying accuracy and classification metrics.

Uploaded by

Gaurang Rane
Copyright
© © All Rights Reserved
We take content rights seriously. If you suspect this is your content, claim it here.
Available Formats
Download as DOCX, PDF, TXT or read online on Scribd
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import numpy as np

import pandas as pd

from sklearn.model_selection import train_test_split

from sklearn.feature_extraction.text import CountVectorizer

from sklearn.naive_bayes import MultinomialNB

from sklearn.pipeline import make_pipeline

from sklearn.metrics import accuracy_score, classification_report

from sklearn.datasets import load_files

import nltk

from nltk.corpus import movie_reviews

nltk.download('movie_reviews')

X = [" ".join(movie_reviews.words(fileid)) for fileid in movie_reviews.fileids()]

y = [1 if fileid.startswith("pos") else 0 for fileid in movie_reviews.fileids()]

X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, random_state=42)

model = make_pipeline(CountVectorizer(stop_words='english'), MultinomialNB())

model.fit(X_train, y_train)

y_pred = model.predict(X_test)

new_review = ["The movie was fantastic! I really enjoyed it."]

prediction = model.predict(new_review)

print(f"Predicted Sentiment: {'Positive' if prediction[0] == 1 else 'Negative'}")

accuracy = accuracy_score(y_test, y_pred)

print(f"Accuracy: {accuracy:.2f}")

print(classification_report(y_test, y_pred))

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