Sales Prediction

We can use text posted on social media platforms for the prediction of future sales, e.g., movie sales.

• Weekly movie data annual_mojo_weekly_full.csv (including weekend gross): scraped from here https://www.boxofficemojo.com/year/:

• Index reddit data with Elasticsearch discussing these movies: from here https://files.pushshift.io/reddit/

  cd reddit_data
  python create_index.py <index_name>
  

• Download sentiment dictionary for feature extraction, e.g., Subjectivity Lexicon: from here http://mpqa.cs.pitt.edu/#subj_lexicon
• Predict future change in gross income based on discusssions from Reddit, see run.py for configuration, e.g.:

  from train_rolling import ClassificatorBuilder
  from data_loader import DataMovie, LexiconTokenizer
  
  #for L2 for the current model
  alpha = 0.01 
  
  #for L2 for the previous model
  beta = 0.01
  
  #we have a regression problem, nb_classes = 1
  nb_classes = 1 
  
  #movie data scraped from here: https://www.boxofficemojo.com/year/
  data = DataMovie("annual_mojo_weekly_full.csv", min_year = 2009, max_year=2018)
  
  #word list for feature extraction, e.g., Subjectivity Lexicon from http://mpqa.cs.pitt.edu/#subj_lexicon
  lexTokenizer = LexiconTokenizer("subjclueslen1-HLTEMNLP05.tff")
  
  #reddit data from https://files.pushshift.io/reddit/ is indexed using Elasticsearch
  index_name = 'reddit-index'
  
  cb = ClassificatorBuilder().add_field('data', data)\
                              .add_field('tokenizer', lexTokenizer)\
                              .add_field('index_name', index_name)\
                              .add_field('train_min', 2009)\ #which data for training?
                              .add_field('train_max', 2009)\
                              .add_field('test_min', 2010)\
                              .add_field('test_max', 2010)\ #which data for testing?
                              .add_field('nb_classes', nb_classes)\
                              .add_field('alpha', alpha)\
                              .add_field('beta', beta)\
                              .add_field('rolling', True)\ #apply rolling transfer learning?
  
  
  cb.classificator.train_rolling()