Multilingual Twitter Sentiment Classification: The Role of Human Annotators
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DOI: 10.1371/journal.pone.0155036
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References listed on IDEAS
- Fabiana Zollo & Petra Kralj Novak & Michela Del Vicario & Alessandro Bessi & Igor Mozetič & Antonio Scala & Guido Caldarelli & Walter Quattrociocchi, 2015. "Emotional Dynamics in the Age of Misinformation," PLOS ONE, Public Library of Science, vol. 10(9), pages 1-22, September.
- Gabriele Ranco & Darko Aleksovski & Guido Caldarelli & Miha Grčar & Igor Mozetič, 2015. "The Effects of Twitter Sentiment on Stock Price Returns," PLOS ONE, Public Library of Science, vol. 10(9), pages 1-21, September.
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- Vuk Batanović & Miloš Cvetanović & Boško Nikolić, 2020. "A versatile framework for resource-limited sentiment articulation, annotation, and analysis of short texts," PLOS ONE, Public Library of Science, vol. 15(11), pages 1-30, November.
- Peter Gabrovšek & Darko Aleksovski & Igor Mozetič & Miha Grčar, 2017. "Twitter sentiment around the Earnings Announcement events," PLOS ONE, Public Library of Science, vol. 12(2), pages 1-21, February.
- Chansiri, Karikarn & Wei, Xinyu & Chor, Ka Ho Brian, 2024. "Using natural language processing approaches to characterize professional experiences of child welfare workers," Children and Youth Services Review, Elsevier, vol. 166(C).
- Paweł Matuszewski, 2023. "How to prepare data for the automatic classification of politically related beliefs expressed on Twitter? The consequences of researchers’ decisions on the number of coders, the algorithm learning pro," Quality & Quantity: International Journal of Methodology, Springer, vol. 57(1), pages 301-321, February.
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