import nltk
import sys

class Analyzer():
    """Implements sentiment analysis."""



    def __init__(self, positives, negatives):
        """Initialize Analyzer."""

        self.Pwords = list()
        self.Nwords= list()
        self.P = open(positives, "r")
        self.N = open(negatives, "r")
        self.load (self.P)
        self.load (self.N)





    def analyze(self, text):
        """Analyze text for sentiment, returning its score."""

        analysis = set()
        from nltk.tokenize import TweetTokenizer
        TokenizerFunction = TweetTokenizer(strip_handles=True, reduce_len=True)
        for a in range (len(text)):
            num = 0
            words = TokenizerFunction.tokenize (text[a])
            for i in range (len(words)):
                temp = Analyzer.check(self, words[i])
                num = num + temp
            analysis.add(num)
        return analysis


    def load(self, dictionary):
        for line in dictionary:
            if dictionary == self.P:
               self.Pwords.append(line.rstrip("\n"))
            if dictionary == self.N:
                self.Nwords.append(line.rstrip("\n"))

        dictionary.close()


    def check (self, checker):
        if checker in self.Pwords:
            return 1
        if checker in self.Nwords:
                return -1
        else:
            return 0