alialavia

"""
To evaluate the good or bad score of a tweet, we first tokenize the tweet, and then 
stemmize each word in our tweet. We also associate each stem with positive and negative values, 
respectively, using a dictionary.

Finally, we caculate the average word weight of a tweet, and decide if it's a good or bad one 
based on that.

"""
import json

alialavia

{
  "thank": 1.0,
  "to": 0.0,
  "the": 0.0,
  "histor": 0.5,
  "cut": 0.0,
  "that": 0.0,
  "I": 0.0,
  "sign": 0.0,

alialavia

"""
To evaluate the good or bad score of a tweet, we first tokenize the tweet, and then 
stemmize each word in our tweet. We also associate each stem with positive and negative values, 
respectively, using a dictionary.

Finally, we caculate the average word weight of a tweet, and decide if it's a good or bad one 
based on that.

"""

alialavia

"""
To evaluate the good or bad score of a tweet, we first split our tweet. 
We then associate each word with positive and negative values, respectively, using a dictionary.

Finally, we caculate the average word weight of a tweet, and decide if it's a good or bad one 
based on that.

"""

# Break down a string into words

alialavia

"""
To evaluate the good or bad score of a tweet, we count the number of good and
bad words in it.

if a word is good, increase the value of good_words by one
else if a word is bad, increase the value of bad_words by one
if good_words > bad_words then it's a good tweet otherwise it's a bad tweet

"""

alialavia

tweet_string = "Thanks to the historic TAX CUTS that I signed into law, your paychecks are going way UP, your taxes are going way DOWN, and America is once again OPEN FOR BUSINESS!"
tweet_words = tweet_string.split()
number_of_words = len(tweet_words)
print(tweet_words)
print("Number of words in this tweet is: " + str(number_of_words))

# Iterate through the words in the tweet string
print("Words in the tweet are:")
for w in tweet_words:
    len_of_w = len(w)