Sentiment analysis methods for understanding large-scale texts: A case for using continuum-scored words and word shift graphs
A. J. Reagan, B. F. Tivnan, J. R. Williams, C. M. Danforth, and P. S. Dodds
EPJ Data Science, 6, 28, 2017
Times cited: 111
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 8 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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BibTeX:
@Article{reagan2017a,
author = {Reagan, Andrew J. and Tivnan, Brian F. and Williams,
Jake Ryland and Danforth, Christopher M. and Dodds,
Peter Sheridan},
title = {Sentiment analysis methods for understanding
large-scale texts: {A} case for using
continuum-scored words and word shift graphs},
journal = {EPJ Data Science},
year = {2017},
key = {langauge},
volume = {6},
}