Created a method that exports term counting, highest sum and score to
weka's file (arff)
'create_histogram' method refactored to get more flexibility in create
histograms in plotly
created the 'is_doc_positive' method to determine if a document is positive
refatored to 'get_arff_file' and inserted 'polarity' attribute
changed the arff module from 'arff' to 'liac arff'
added more two features to arff files: document size and ngrams size.
added a method to normalize a list of numbers for future features
Created methods in util do normalize numbers and list of numbers and
a method to normalize arff data
changed normalization to total of tokens and total of ngrams selected
added a pre-process method into model to extract verbs due to Ohana, 2009.
Also refactored some variables names.
added 'Sum of positive and negative scores for Verbs' and
'Ratio of overall score per total terms found' for verbs
added a "positive to negative ratio feature" of ngrams and verbs sums
added the following features 'ngrams_count_pos_to_neg_ratio' and
'ngrams_score_pos_to_neg_ratio'
fixed bug in ngrams_sum_pos_to_neg_ratio
added the following features:

ngrams_term_count_by_doc_size_ratio
ngrams_sum_by_doc_size_ratio
verbs_sum_by_doc_size_ratio
ngrams_term_count_by_ngrams_ratio
ngrams_sum_by_ngrams_ratio
verbs_sum_by_ngrams_ratio
added negation percentage feature
Added the word position in model and util
changed the pre-process-adverbs method to fits with insertion of word
positition
changed the pre_process_verbs method to fits util changes
changed the way how bigrams are parsed due to insertion of word index