smoothing_parameter = 0.369
    no_words_pos_rev = 0
    word_in_pos_rev ={}

    no_words_neg_rev = 0
    word_in_neg_rev ={}

    unique_words = set()

    label = 0
    for review in train_set:
        # review is positive
        if train_labels[label] == 1:
            for word in review:
                unique_words.add(word)

                no_words_pos_rev += 1
                if word in word_in_pos_rev:
                    word_in_pos_rev[word] += 1
                else:
                    word_in_pos_rev[word] = 1
        # review is negative
        else:
            for word in review:
                unique_words.add(word)

                no_words_neg_rev += 1
                if word in word_in_neg_rev:
                    word_in_neg_rev[word] += 1
                else:
                    word_in_neg_rev[word] = 1


    sentiment_of_reviews = []
    for review in dev_set:
        positive_sentiment = np.log(pos_prior)
        negative_sentiment = np.log(1-pos_prior)
        for word in review:
            denomenator_pos = no_words_pos_rev + smoothing_parameter*(len(unique_words)+1)
            denomenator_neg = no_words_neg_rev + smoothing_parameter*(len(unique_words)+1)

            if word in word_in_pos_rev:
                # P(word_i|Positive) = (number of times word occured in a positive review) / (number of words in positive reviews)
                positive_sentiment += np.log( (word_in_pos_rev[word] + np.log(smoothing_parameter) ) / denomenator_pos )
            else:
                positive_sentiment += np.log(smoothing_parameter/denomenator_pos)

            if word in word_in_neg_rev:
                negative_sentiment += np.log( (word_in_neg_rev[word] + np.log(smoothing_parameter) ) / denomenator_neg )
            else:
                negative_sentiment += np.log(smoothing_parameter/denomenator_neg)
        if positive_sentiment > negative_sentiment:
            sentiment_of_reviews.append(1)
        else:
            sentiment_of_reviews.append(0)
    print(sentiment_of_reviews)
    return sentiment_of_reviews