Abstract
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.
(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.
| Original language | English |
|---|---|
| Title of host publication | Proceedings of the Workshop on Language Analysis for Social Media (LASM) |
| Place of Publication | Gothenburg, Sweden |
| Publisher | Association for Computational Linguistics |
| Pages | 26–34 |
| Number of pages | 8 |
| DOIs | |
| Publication status | Published - 2014 |