def get_tweet_word_frequencies(word_vocab_dict, tweet_text):
	"""
	Receives a count of sentiment frequencies for each word in the corpus and a 
	tweet then it computes the word frequencies to it

	Parameters:
	word_vocab_dict (dict): contains words and sentiment as keys and 
	total count in the corpus as value
	tweet_text (str): preprocessed tweet to generate word frequencies

	Returns:
	list(int): two integers, for positive and negative word frequencies
	in the tweet
	"""
	# initialize total frequency variables
	pos_total_freq = 0
	neg_total_freq = 0

	for word in tweet_text:
		# for each word in the tweet, get its positive and negative frequency
		neg_freq = word_vocab_dict.get((word, 0), 0)
		pos_freq = word_vocab_dict.get((word, 1), 0)

		# sum positive and negative frequencies of current word to running total
		neg_total_freq += neg_freq
		pos_total_freq += pos_freq

	return [neg_total_freq, pos_total_freq]