TY - GEN
T1 - Detecting sarcasm from students' feedback in Twitter
AU - Altrabsheh, Nabeela
AU - Cocea, Mihaela
AU - Fallahkhair, Sanaz
N1 - The final publication is available at link.springer.com
PY - 2015/9/14
Y1 - 2015/9/14
N2 - 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.
AB - 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.
U2 - 10.1007/978-3-319-24258-3_57
DO - 10.1007/978-3-319-24258-3_57
M3 - Conference contribution with ISSN or ISBN
VL - 9307
T3 - Lecture Notes in Computer Science
SP - 551
EP - 555
BT - 10th European Conference on Technology Enhanced Learning, EC-TEL
PB - Springer
CY - Toledo, Spain
T2 - 10th European Conference on Technology Enhanced Learning, EC-TEL
Y2 - 14 September 2015
ER -
