research-article

Sarcasm Detection on Flickr Using a CNN

Authors:

Anthony J. ClarkAuthors Info & Claims

ICCBD '18: Proceedings of the 2018 International Conference on Computing and Big Data

Pages 56 - 61

https://doi.org/10.1145/3277104.3277118

Published: 08 September 2018 Publication History

Abstract

Sarcasm is an important aspect of human communication. However, it is often difficult to detect or understand this sentiment because the literal meaning conveyed in communication is opposite of the intended meaning. Though the field of sentiment analysis is well studied, sarcasm has often been ignored by the research community. So far, to detect sarcasm on social media, studies have largely focused upon textual features. However, visual cues are an important part of sarcasm. In this paper, we present a convolutional neural network based model for detecting sarcasm based on images shared on a popular social photo sharing site, Flickr.

References

[1]

2005. Flickr: Explore interesting contens around Flickr. Retrieved May 19, 2018 from https://www.flickr.com/explore/interesting/

[2]

2018. Definition of Sarcasm by Merriam-Webster. Retrieved May 19, 2018 from https://www.merriam-webster.com/dictionary/sarcasm

[3]

2018. Grice's Maxims. Retrieved May 19, 2018 from https://www.sas.upenn.edu/ ~haroldfs/dravling/grice.html

[4]

David Bamman and Noah A Smith. 2015. Contextualized Sarcasm Detection on Twitter. In Proceedings of the Ninth International AAAI Conference on Web and Social Media. 574--577.

[5]

Mathieu Cliche. 2014. The sarcasm detector. Retrieved May 19, 2018 from http://www.thesarcasmdetector.com/

[6]

Elena Filatova. 2012. Irony and Sarcasm: Corpus Generation and Analysis Using Crowdsourcing. In International Conference on Language Resources and Evaluation. Citeseer, 392--398.

[7]

Vasavi Gajarla and Aditi Gupta. 2015. Emotion Detection and Sentiment Analysis of Images. Georgia Institute of Technology (2015).

[8]

Aniruddha Ghosh and Tony Veale. 2016. Fracking sarcasm using neural network. In Proceedings of the 7th Workshop on Computational Approaches to Subjectivity, Sentiment and Social Media Analysis. 161--169.

[9]

Tamal Ghosh. 2016. Sarcasm Detection - Machine learning perspective. Retrieved May 27, 2018 from https://www.sas.com/content/dam/SAS/en_us/doc/event/analytics-experience-2016/sarcasm-detection-machine-learning-perspective.pdf

[10]

Lisa M Given. 2008. The Sage encyclopedia of qualitative research methods. Sage Publications.

[11]

Roberto González-Ibánez, Smaranda Muresan, and Nina Wacholder. 2011. Identifying sarcasm in Twitter: a closer look. In Proceedings of the 49th Annual Meeting of the Association for Computational Linguistics: Human Language Technologies: Short Papers. Volume 2. Association for Computational Linguistics, 581--586.

Digital Library

[12]

Leo A Goodman. 1961. Snowball sampling. The annals of mathematical statistics (1961), 148--170.

[13]

Mikhail Khodak, Nikunj Saunshi, and Kiran Vodrahalli. 2017. A large self-annotated corpus for sarcasm. arXiv preprint arXiv:1704.05579 (2017).

[14]

Chun-Che Peng, Mohammad Lakis, and Jan Wei Pan. 2015. Detecting Sarcasm in Text. (2015).

[15]

Artur Przelaskowski. 2010. The role of sparse data representation in semantic image understanding. In International Conference on Computer Vision and Graphics. Springer, 69--80.

Digital Library

[16]

Antonio Reyes, Paolo Rosso, and Tony Veale. 2013. A multidimensional approach for detecting irony in twitter. Language resources and evaluation 47, 1 (2013), 239--268.

Digital Library

[17]

Ellen Riloff, Ashequl Qadir, Prafulla Surve, Lalindra De Silva, Nathan Gilbert, and Ruihong Huang. 2013. Sarcasm as contrast between a positive sentiment and negative situation. In Proceedings of the 2013 Conference on Empirical Methods in Natural Language Processing. 704--714.

[18]

Sebastian Ruder. 2016. An overview of gradient descent optimization algorithms. arXiv preprint arXiv:1609.04747 (2016).

[19]

Yutaka Sasaki et al. 2007. The truth of the F-measure. Teach Tutor mater 1, 5 (2007).

[20]

Rossano Schifanella, Paloma de Juan, Joel Tetreault, and Liangliang Cao. 2016. Detecting sarcasm in multimodal social platforms. In Proceedings of the 2016 ACM on Multimedia Conference. ACM, 1136--1145.

Digital Library

[21]

Stefan Siersdorfer, Enrico Minack, Fan Deng, and Jonathon Hare. 2010. Analyzing and predicting sentiment of images on the social web. In Proceedings of the 18th ACM international conference on Multimedia. ACM, 715--718.

Digital Library

[22]

Reid Swanson, Stephanie Lukin, Luke Eisenberg, Thomas Chase Corcoran, and Marilyn A Walker. 2017. Getting reliable annotations for sarcasm in online dialogues. arXiv preprint arXiv:1709.01042 (2017).

[23]

Joseph Tepperman, David Traum, and Shrikanth Narayanan. 2006. " Yeah Right": Sarcasm Recognition for Spoken Dialogue Systems. In Ninth International Conference on Spoken Language Processing.

[24]

Marilyn A Walker, Jean E Fox Tree, Pranav Anand, Rob Abbott, and Joseph King. 2012. A Corpus for Research on Deliberation and Debate. In International Conference on Language Resources and Evaluation. 812--817.

[25]

Byron C Wallace, Laura Kertz, Eugene Charniak, et al. 2014. Humans require context to infer ironic intent (so computers probably do, too). In Proceedings of the 52nd Annual Meeting of the Association for Computational Linguistics (Volume 2: Short Papers), Vol. 2. 512--516.

Cited By

Alghofaily HFkih F(2023)Sarcasm Detection on Twitter: a Comparative Survey2023 Congress in Computer Science, Computer Engineering, & Applied Computing (CSCE)10.1109/CSCE60160.2023.00110(642-649)Online publication date: 24-Jul-2023
https://doi.org/10.1109/CSCE60160.2023.00110
Petilli MGünther FMarelli M(2022)The Flickr frequency norms: What 17 years of images tagged online tell us about lexical processingBehavior Research Methods10.3758/s13428-022-02031-y56:1(126-147)Online publication date: 12-Dec-2022
https://doi.org/10.3758/s13428-022-02031-y
Li GLin FChen WLiu B(2022)Affection Enhanced Relational Graph Attention Network for Sarcasm DetectionApplied Sciences10.3390/app1207363912:7(3639)Online publication date: 4-Apr-2022
https://doi.org/10.3390/app12073639
Show More Cited By

Index Terms

Sarcasm Detection on Flickr Using a CNN

Recommendations

Automatic Sarcasm Detection: A Survey

Automatic sarcasm detection is the task of predicting sarcasm in text. This is a crucial step to sentiment analysis, considering prevalence and challenges of sarcasm in sentiment-bearing text. Beginning with an approach that used speech-based features, ...
Detecting Sarcasm in Multimodal Social Platforms
MM '16: Proceedings of the 24th ACM international conference on Multimedia

Sarcasm is a peculiar form of sentiment expression, where the surface sentiment differs from the implied sentiment. The detection of sarcasm in social media platforms has been applied in the past mainly to textual utterances where lexical indicators (...
Sarcasm detection on Facebook: a supervised learning approach
ICMI '18: Proceedings of the 20th International Conference on Multimodal Interaction: Adjunct

Sarcasm is a common feature of user interaction on social networking sites. Sarcasm differs with typical communication in alignment of literal meaning with intended meaning. Humans can recognize sarcasm from sufficient context information including from ...

Comments

Please enable JavaScript to view thecomments powered by Disqus.

Information & Contributors

Information

Published In

cover image ACM Other conferences

ICCBD '18: Proceedings of the 2018 International Conference on Computing and Big Data

September 2018

103 pages

ISBN:9781450365406

DOI:10.1145/3277104

Copyright © 2018 ACM.

Permission to make digital or hard copies of all or part of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for components of this work owned by others than ACM must be honored. Abstracting with credit is permitted. To copy otherwise, or republish, to post on servers or to redistribute to lists, requires prior specific permission and/or a fee. Request permissions from [email protected]

Publisher

Association for Computing Machinery

New York, NY, United States

Publication History

Published: 08 September 2018

Permissions

Request permissions for this article.

Request Permissions

Check for updates

Author Tags

Qualifiers

Research-article
Research
Refereed limited

Conference

ICCBD '18

ICCBD '18: 2018 International Conference on Computing and Big Data

September 8 - 10, 2018

SC, Charleston, USA

Contributors

Other Metrics

View Article Metrics

Bibliometrics & Citations

Bibliometrics

Article Metrics

21
Total Citations
View Citations
408
Total Downloads

Downloads (Last 12 months)31
Downloads (Last 6 weeks)3

Reflects downloads up to 19 Nov 2024

Other Metrics

View Author Metrics

Citations

Cited By

Alghofaily HFkih F(2023)Sarcasm Detection on Twitter: a Comparative Survey2023 Congress in Computer Science, Computer Engineering, & Applied Computing (CSCE)10.1109/CSCE60160.2023.00110(642-649)Online publication date: 24-Jul-2023
https://doi.org/10.1109/CSCE60160.2023.00110
Petilli MGünther FMarelli M(2022)The Flickr frequency norms: What 17 years of images tagged online tell us about lexical processingBehavior Research Methods10.3758/s13428-022-02031-y56:1(126-147)Online publication date: 12-Dec-2022
https://doi.org/10.3758/s13428-022-02031-y
Li GLin FChen WLiu B(2022)Affection Enhanced Relational Graph Attention Network for Sarcasm DetectionApplied Sciences10.3390/app1207363912:7(3639)Online publication date: 4-Apr-2022
https://doi.org/10.3390/app12073639
Sura KDamera SPosonia A(2022)Sarcasm Detection in News Headlines2022 6th International Conference on Computing Methodologies and Communication (ICCMC)10.1109/ICCMC53470.2022.9754165(1467-1473)Online publication date: 29-Mar-2022
https://doi.org/10.1109/ICCMC53470.2022.9754165
Bhat AChauhan A(2022)A Deep Learning based approach for MultiModal Sarcasm Detection2022 4th International Conference on Advances in Computing, Communication Control and Networking (ICAC3N)10.1109/ICAC3N56670.2022.10074506(2523-2528)Online publication date: 16-Dec-2022
https://doi.org/10.1109/ICAC3N56670.2022.10074506
Bhat AChauhan A(2022)MultiModal Sarcasm Detection: A Survey2022 IEEE Delhi Section Conference (DELCON)10.1109/DELCON54057.2022.9753058(1-7)Online publication date: 11-Feb-2022
https://doi.org/10.1109/DELCON54057.2022.9753058
Shaik TTao XLi YDann CMcDonald JRedmond PGalligan L(2022)A Review of the Trends and Challenges in Adopting Natural Language Processing Methods for Education Feedback AnalysisIEEE Access10.1109/ACCESS.2022.317775210(56720-56739)Online publication date: 2022
https://doi.org/10.1109/ACCESS.2022.3177752
Chandrasekaran GHemanth D(2022)Efficient Visual Sentiment Prediction Approaches Using Deep Learning ModelsKnowledge Graphs and Semantic Web10.1007/978-3-030-91305-2_20(260-272)Online publication date: 1-Jan-2022
https://doi.org/10.1007/978-3-030-91305-2_20
AKKAYA A(2021)Sarcastic Text Detection Using KerasComputer Science10.53070/bbd.990890Online publication date: 20-Sep-2021
https://doi.org/10.53070/bbd.990890
Venkatesh BVishwas H(2021)Real Time Sarcasm Detection on Twitter using Ensemble Methods2021 Third International Conference on Inventive Research in Computing Applications (ICIRCA)10.1109/ICIRCA51532.2021.9544841(1292-1297)Online publication date: 2-Sep-2021
https://doi.org/10.1109/ICIRCA51532.2021.9544841
Show More Cited By

View Options

Login options

Check if you have access through your login credentials or your institution to get full access on this article.

Full Access

Get this Publication

View options

PDF

View or Download as a PDF file.

eReader

View online with eReader.

Media

Figures

Other

Tables

View Table of Contents