Detecting sarcasm from students' feedback in Twitter

Nabeela Altrabsheh, Mihaela Cocea, Sanaz Fallahkhair

Research output: Chapter in Book/Report/Conference proceedingConference contribution


Sarcasm is a sophisticated form of act where one says or writes the opposite of what they mean. Sarcasm is a common issue in sentiment analysis and detecting it is a challenge. While models for sarcasm detection have been proposed for general purposes (e.g. Twitter data, Amazon reviews), there is no research addressing this issue in an educational context, despite the increased use of social media in education. In this paper we experiment with several machine learning techniques, features and preprocessing levels to identify sarcasm from students' feedback collected via Twitter.
Original languageEnglish
Title of host publicationDesign for teaching and learning in a networked world
Subtitle of host publication10th European Conference on Technology Enhanced Learning, EC-TEL 2015, Toledo, Spain, September 15–18, 2015, proceedings
EditorsGránne Conole, Tomaž Klobučar , Christoph Rensing, Johannes Konert, Élise Lavoué
Place of PublicationCham
ISBN (Electronic)9783319242583
ISBN (Print)9783319242576
Publication statusPublished - 14 Sept 2015

Publication series

NameLecture Notes in Computer Science (LNCS)
ISSN (Print)0302-9743


  • Sarcasm detection
  • Sentiment Analysis
  • Student feedback
  • WNU


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