Anticipointment Detection in Event Tweets

Florian A. Kunneman, Margot van Mulken, A. van den Bosch

Research output: Contribution to journal/periodicalArticleScientificpeer-review

Abstract

We developed a system to detect positive expectation, disappointment, and satisfaction in tweets that refer to events automatically discovered in the Twitter stream. The emotional content shared on Twitter when referring to public events can provide insights into the presumed and experienced quality of the event. We expected to find a connection between positive expectation and disappointment, a succession that is referred to as anticipointment. The application of computational approaches makes it possible to detect the presence and strength of this hypothetical relation for a large number of events. We extracted events from a longitudinal dataset of Dutch Twitter posts, and modeled classifiers to detect emotion in the tweets related to those events by means of hashtag-labeled training data. After classifying all tweets before and after the events in our dataset, we summarized the collective emotions for over 3000 events as the percentage of tweets classified as positive expectation (in anticipation), disappointment and satisfaction (in hindsight). Only a weak correlation of around 0.2 was found between positive expectation and disappointment, while a higher correlation of 0.6 was found between positive expectation and satisfaction. The most anticipointing events were events with a clear loss, such as a canceled event or when the favored sports team had lost. We conclude that senders of Twitter posts might be more inclined to share satisfaction than disappointment after a much anticipated event.
Original languageEnglish
JournalInternational Journal on Artificial Intelligence Tools
Volume29
Issue number2
DOIs
Publication statusPublished - 01 Apr 2020

Keywords

  • Event detection
  • emotion detection
  • Twitter

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