Function to calculate sentiment based on word-dictionary

import nltk
from nltk.tokenize import word_tokenize

# Download the punkt tokenizer
nltk.download('punkt')

# Example lists of positive and negative words
pos_words = {"good", "successful", "positive", "bright", "excellent", "great", "happy", "sunny"}
neg_words = {"lockdown", "affected", "pandemic", "bad", "poor", "sad"}

def calc_sentiment_based_on_word_dict(text):
    sentiment_score = 0
    words = word_tokenize(text)
    for word in words:
        if word in pos_words:
            print('pos:', word)
            sentiment_score += 1
        if word in neg_words:
            print('neg:', word)
            sentiment_score -= 1
    return sentiment_score / len(words)

# Text to analyze
text = 'The weather is great and everyone is happy with the sunny day.'
sentiment = calc_sentiment_based_on_word_dict(text)
print('The sentiment score of this text is: {:.2f}'.format(sentiment))