gistfile1.py

from nltk.classify.api import ClassifierI
import random

class SimpleClassifier(ClassifierI): 

    def __init__(self, pos, neg): 
        self._pos = pos 
        self._neg = neg 

    def classify(self, words): 
        score = 0
        
        # add code here that assigns an appropriate value to score
        
        return "N" if score < 0 else "P" 

    def batch_classify(self, docs): 
        return [self.classify(doc.words() if hasattr(doc, 'words') else doc) for doc in docs] 

    def labels(self): 
        return ("P", "N")

#Example usage:

book_classifier = SimpleClassifier(positive_book_words_list, negative_book_words_list)