text: var Sentiment = require('sentiment');
  var sentiment = new Sentiment();
  
  const vader = require('vader-sentiment');
  
  const natural = require('natural');
  var Analyzer = natural.SentimentAnalyzer;
  var stemmer = natural.PorterStemmer;
  var analyzer = new Analyzer("English", stemmer, "afinn");
  var tokenizer = new natural.WordTokenizer();
  
  XLSX = require('xlsx');
  var fname = process.argv[2];
  var wb = XLSX.readFile(fname);
  var ws = wb.Sheets[wb.SheetNames[0]];
  var range = XLSX.utils.decode_range(ws['!ref']);
  
  var first_col = range.s.c;
  var last_col = range.e.c;
  
  var first_row = range.s.r;
  var last_row = range.e.r;
  
  console.log([ws['B1'].v, ws['C1'].v, 'Score', 'Comparative', 'Pos Terms', 'Neg Terms', 'Vader Pos', 'Vader Neutral', 'Vader Neg', 'Vader Compound', 'Natural Sentiment'].join("\t"));
  
  for(rowNum = range.s.r+1; rowNum <= range.e.r; rowNum++){
      row = [];
      var val2 = ws['B'+(rowNum+1)].v;
      var val3 = ws['C'+(rowNum+1)].v;
      // Now analyze with Sentiment
      var sent = sentiment.analyze(val3);
      ws['D'+(rowNum+1)] = sent.score;
      // ...now with vader-sentiment
      const vsent = vader.SentimentIntensityAnalyzer.polarity_scores(val3);
      // ...now with NaturalNode/natural
      const nsent = analyzer.getSentiment(tokenizer.tokenize(val3));
      // ...output results
      console.log([val2, val3, sent.score, sent.comparative, sent.positive, sent.negative, vsent.pos, vsent.neu, vsent.neg, vsent.compound, nsent].join("\t"));
  }
  
  