Sentiment analysis methods for understanding large-scale texts: a case for using continuum-scored words and word shift graphs

Overview of attention for article published in EPJ Data Science, October 2017

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In the top 5% of all research outputs scored by Altmetric
Among the highest-scoring outputs from this source (#37 of 594)
High Attention Score compared to outputs of the same age (97th percentile)
Good Attention Score compared to outputs of the same age and source (75th percentile)

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news: 1 news outlet
blogs: 2 blogs
twitter: 91 X users

facebook: 1 Facebook page
googleplus: 1 Google+ user

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So far, Altmetric has seen 114 X posts from 91 X users, with an upper bound of 322,565 followers.

@ryanjgallag @damian0604 @peterdodds Thanks for the nice thread Ryan! Closest thing we have done is this work by @andyreagan who may know of another reference https://t.co/5TKRKNqO1Y

@GolinoHudson @sivavaid We’ve been very happy with EPJ Data Science for sentiment related work, e.g. https://t.co/5TKRKNqO1Y

@ryanjgallag Yes. Sentiment of individual tweets is super murky. For large collections of tweets, our dictionary method comparison: https://t.co/5TKRKNqO1Y https://t.co/HMu0UELk2x

RT @compstorylab: @SimonDeDeo we compared a few dozen lists of categorical & continuous scores here https://t.co/WQPUHQsA6p

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