Benchmarking sentiment analysis methods for large-scale texts: A case for using continuum-scored words and word shift graphs

Andrew J. Reagan, Brian Tivnan, Jake Ryland Williams, Christopher M. Danforth, Peter Sheridan Dodds
12/2/2015
cs.CL

Official code: andyreagan/sentiment-analysis-comparison (16 stars, last commit 2017-06-08).

Abstract

The emergence and global adoption of social media has rendered possible the real-time estimation of population-scale sentiment, bearing profound implications for our understanding of human behavior. Given the growing assortment of sentiment measuring instruments, comparisons between them are evidently required. Here, we perform detailed tests of 6 dictionary-based methods applied to 4 different corpora, and briefly examine a further 20 methods. We show that a dictionary-based method will only perform both reliably and meaningfully if (1) the dictionary covers a sufficiently large enough portion of a given text's lexicon when weighted by word usage frequency; and (2) words are scored on a continuous scale.

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Code for the paper "Benchmarking sentiment analysis methods for large-scale texts: A case for using continuum-scored words and word shift graphs"

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Cite this paper

@article{reagan2015benchmarking,
  title  = {Benchmarking sentiment analysis methods for large-scale texts: A case for using continuum-scored words and word shift graphs},
  author = {Andrew J. Reagan and Brian Tivnan and Jake Ryland Williams and Christopher M. Danforth and Peter Sheridan Dodds},
  year   = {2015},
  eprint = {1512.00531},
  archivePrefix = {arXiv},
  url    = {https://arxiv.org/abs/1512.00531}
}

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