Temporal patterns of happiness and information in a global social network: hedonometrics and Twitter

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Temporal patterns of happiness and information in a global social network: hedonometrics and Twitter
Authors: Peter Sheridan Dodds, Kameron Decker Harris, Isabel M. Kloumann, Catherine A. Bliss, Christopher M. Danforth
Citation: PLOS ONE 6 (12): e26752. 2011
Database(s): arXiv (Arxiv/1101.5120) Google Scholar cites PubMed (PMID/22163266)
DOI: 10.1371/journal.pone.0026752.
PMCID:3233600
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Temporal patterns of happiness and information in a global social network: hedonometrics and Twitter

Contents

[edit] Methods

  1. labMT is used for sentiment analysis. It is distributed in the supplementary material. The over 10'000 words were labeled with Amazon's Mechanical Turk.

[edit] Related papers

  1. Patterns of emotional blogging and emergence of communities: agent-based model on bipartite networks
  2. The QWERTY effect: how typing shapes the meanings of words

[edit] Critique

  1. How can the researchers know that there is a one-to-one correspondence between Twitter sentiment and happiness?

[edit] See also

  1. Large-scale sentiment analysis for news and blogs
  2. Tweetin' in the rain: exploring societal-scale effects of weather on mood
  3. Twittermood
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