The (Un)Predictability of Emotional Hashtags in Twitter
s.l. : Association for Computational Linguistics
InProceedings of the 5th Workshop on Language Analysis for Social Media (LASM) @ EACL 2014, pp. 26-34
5th Workshop on Language Analysis for Social Media (LASM), 26 april 2014
Article in monograph or in proceedings
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Communicatie- en informatiewetenschappen
Proceedings of the 5th Workshop on Language Analysis for Social Media (LASM) @ EACL 2014
SubjectADNEXT (Adaptive Information Extraction over Time); Language & Speech Technology; Language in Society; Nederlab; Persuasive Communication; Style and Persuasive Power: Language Intensity; The changing dynamics of news (project of: ADNEXT (Adaptive Information Extraction over Time (is project of COMIC)); Stijl en overtuigingskracht: Taalintensiteit
Hashtags in Twitter posts may carry different semantic payloads. Their dual form (word and label) may serve to categorize the tweet, but may also add content to the message, or strengthen it. Some hashtags are related to emotions. In a study on emotional hashtags in Dutch Twitter posts we employ machine learning classifiers to test to what extent tweets that are stripped from their hashtag could be reassigned to this hashtag. About half of the 24 tested hashtags can be predicted with AUC scores of .80 or higher. However, when we apply the three best-performing classifiers to unseen tweets that do not carry the hashtag but might have carried it according to human annotators, the classifiers manage to attain a precision-at-250 of .7 for only two of the hashtags. We observe that some hashtags are predictable from their tweets, and strengthen the emotion already expressed in the tweets. Other hashtags are added to messages that do not predict them, presumably to provide emotional information that was not yet in the tweet.
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