TY - GEN
T1 - Identifying Humor in Reviews using Background Text Sources
AU - Morales, Alex
AU - Zhai, Chengxiang
N1 - Funding Information:
The first author was supported by the University of Illinois, Urbana-Champaign College of Engineer- ing’s Support for Underrepresented Groups in Engineering (SURGE) Fellowship and the Graduate College’s Graduate Distinguished Fellowship.
Publisher Copyright:
© 2017 Association for Computational Linguistics.
Copyright:
Copyright 2020 Elsevier B.V., All rights reserved.
PY - 2017/1/1
Y1 - 2017/1/1
N2 - We study the problem of automatically identifying humorous text from a new kind of text data, i.e., online reviews. We propose a generative language model, based on the theory of incongruity, to model humorous text, which allows us to leverage background text sources, such as Wikipedia entry descriptions, and enables construction of multiple features for identifying humorous reviews. Evaluation of these features using supervised learning for classifying reviews into humorous and non-humorous reviews shows that the features constructed based on the proposed generative model are much more effective than the major features proposed in the existing literature, allowing us to achieve almost 86% accuracy. These humorous review predictions can also supply good indicators for identifying helpful reviews.
AB - We study the problem of automatically identifying humorous text from a new kind of text data, i.e., online reviews. We propose a generative language model, based on the theory of incongruity, to model humorous text, which allows us to leverage background text sources, such as Wikipedia entry descriptions, and enables construction of multiple features for identifying humorous reviews. Evaluation of these features using supervised learning for classifying reviews into humorous and non-humorous reviews shows that the features constructed based on the proposed generative model are much more effective than the major features proposed in the existing literature, allowing us to achieve almost 86% accuracy. These humorous review predictions can also supply good indicators for identifying helpful reviews.
UR - http://www.scopus.com/inward/record.url?scp=85054976151&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=85054976151&partnerID=8YFLogxK
U2 - 10.18653/v1/d17-1051
DO - 10.18653/v1/d17-1051
M3 - Conference contribution
T3 - EMNLP 2017 - Conference on Empirical Methods in Natural Language Processing, Proceedings
SP - 492
EP - 501
BT - EMNLP 2017 - Conference on Empirical Methods in Natural Language Processing, Proceedings
PB - Association for Computational Linguistics (ACL)
T2 - 2017 Conference on Empirical Methods in Natural Language Processing, EMNLP 2017
Y2 - 9 September 2017 through 11 September 2017
ER -