2026-06-17T09:02:08Z https://datacatalogue.cessda.eu/oai-pmh/v0/oai

eb4f1f1dd74719d7bf9d85a1597b21a7731fdd311e3ac5142466a5afd5cf01e5 2026-06-02T13:15:05Z language:enopenaire_data

Dataset: tweets and analyses related to the paper 'The (Un)Predictability of Emotional Hashtags in Twitter' Dataset: tweets and analyses related to the paper 'The (Un)Predictability of Emotional Hashtags in Twitter' doi:10.17026/DANS-ZS9-FJ3TRadboud MetisID 553786easy-dataset:72554 F.A. Kunneman C.C. Liebrecht A.P.J. van den Bosch 2017-01-01 DANS Data Station Social Sciences and Humanities 2017-01-01 Arts and HumanitiesComputer and Information ScienceSocial SciencesTwitterEmotionNatural Language ProcessingDocument ClassificationMachine LearningEmotion Detection <p>This dataset features all the tweetids and labels that were used to model the language of 24 hashtags, and test the performance on predicting the hashtags in unseen tweets. This study is described in:</p><p>Kunneman, F.A., Liebrecht, C.C. & Bosch, A.P.J. van den (2014). The (Un)Predictability of Emotional Hashtags in Twitter. In Proceedings of the 5th Workshop on Language Analysis for Social Media (LASM) @ EACL 2014 (pp. 26-34). s.l.: Association for Computational Linguistics, http://hdl.handle.net/2066/127067</p><p>In addition to the train and test data, this dataset includes the most indicative features (words and phrases) for four of the hashtags, as well as the human judgement whether the tweets that contain or are classified with these hashtags convey the presumed emotion of the hashtags.</p><p>Subject period: December 16th 2010 until February 1st 2013</p>Date: start=2013-07-01; end=2013-07-31 (data collection) https://ssh.datastations.nl/oai doi:10.17026/DANS-ZS9-FJ3T 2025-12-03T01:00:46Z ddi:codebook:2_5