VaderSentiment Helper Function

#----------------- FEATURE ENGINEERING ------------------------#

#---------- Sentiment Score with Vader -----------------#

# Instantiate Vader 
analyser = SentimentIntensityAnalyzer()

def polarity_scores_all(tweet):
  '''
  Takes string of text to:
  1. Gets sentiment metrics
  2. Returns negative, neutral, positive 
  and compound scores as lists.
  '''
  neg, neu, pos, compound = [], [], [], []
  analyser = SentimentIntensityAnalyzer()
  
  for text in tweet:
    dict_ = analyser.polarity_scores(text)
    neg.append(dict_['neg'])
    neu.append(dict_['neu'])
    pos.append(dict_['pos'])
    compound.append(dict_['compound'])
  
  return neg, neu, pos, compound 
  
 # Append to your dataset
  
all_scores = polarity_scores_all(train.tidy_tweet.values)
train['neg_scores'] = all_scores[0]
train['neu_scores'] = all_scores[1]
train['pos_scores'] = all_scores[2]
train['compound_scores'] = all_scores[3]