Title: Dataset: tweets and analyses related to the paper 'The (Un)Predictability of Emotional Hashtags in Twitter'
Creators: Kunneman, F.A. ; Liebrecht, C.C. ; Bosch, A.P.J. van den
Date of Archiving: 2017
Archive: DANS EASY
DOI: https://doi.org/10.17026/dans-zs9-fj3t
Related publications: The (un)predictability of emotional hashtags in Twitter
Publication type: Dataset
Access level : open access
Spatial Coverage The Netherlands; Flanders 

Please use this identifier to cite or link to this item : https://hdl.handle.net/2066/174262   https://hdl.handle.net/2066/174262

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Key words: Twitter
Emotion
Natural Language Processing
Document Classification
Machine Learning
Emotion Detection
Organization: Communicatie- en informatiewetenschappen
Abstract:
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: 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 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. Subject period: December 16th 2010 until February 1st 2013
Audience(s): Communication sciences
Computer science
Language and literature studies
Languages used: Dutch; English

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