gracecarrillo

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

#--- Part of Speech Tags (POS)--#

nltk.download('averaged_perceptron_tagger')

pos_family = {  
    'NOUN' : ['NN','NNS','NNP'], # Removed 'NNPS'
    'PRON' : ['PRP','PRP$','WP','WP$'],
    'VERB' : ['VB','VBD','VBG','VBN','VBP','VBZ'],

gracecarrillo

#------------- DEEP LEARNING -----------------#

#-----LSTM Network with Keras------#

#--- Parameters----#

# encodes input sequence dense vectors 
embed_dim = 128

# transforms the vector sequence into a single vector

gracecarrillo

#----------- CROSS-VALIDATION WITH GRID SEARCH ------------------------#

# Naive Bayes Classifier

# combine features
features_tfidf = features_union(tfidf)

# instantiate pipeline object
nb_pipeline = Pipeline([('feats', features_tfidf),  ('clf', MultinomialNB())])

gracecarrillo

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

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

# Instantiate Vader 
analyser = SentimentIntensityAnalyzer()

def polarity_scores_all(tweet):
  '''
  Takes string of text to:

gracecarrillo

#----------------------------- ETL ---------------------------------------#


# Pipeline like preprocessing with helper functions

nltk.download('stopwords')
stop_words = stopwords.words('english')

# cleaning helper function -----------------------------#
def processTweet(tweet):

gracecarrillo

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gracecarrillo

gracecarrillo