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Sarcasm detection on Facebook: a supervised learning approach

Published: 16 October 2018 Publication History

Abstract

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 the various contents available on SNS. Existing literature mainly uses text data to detect sarcasm; though, a few recent studies propose to use image data. To date, no study has focused on user interaction pattern as a source of context information for detecting sarcasm. In this paper, we present a supervised machine learning based approach focusing on both contents of posts (e.g., text, image) and users' interaction on those posts on Facebook.

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cover image ACM Conferences
ICMI '18: Proceedings of the 20th International Conference on Multimodal Interaction: Adjunct
October 2018
62 pages
ISBN:9781450360029
DOI:10.1145/3281151
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]

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Published: 16 October 2018

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Author Tags

  1. Facebook
  2. image
  3. sarcasm
  4. sentiment
  5. supervised learning
  6. text

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  • (2024)Ben-Sarc: A self-annotated corpus for sarcasm detection from Bengali social media comments and its baseline evaluationNatural Language Processing10.1017/nlp.2024.11(1-26)Online publication date: 27-May-2024
  • (2024)Modes and meanings of language use in social mediaHandbook of Social Media Use Online Relationships, Security, Privacy, and Society, Volume 210.1016/B978-0-443-28804-3.00006-5(165-192)Online publication date: 2024
  • (2024)Sarcasm Detection on Social Media Text Using Major Voting Ensemble ApproachProceedings of the 4th International Conference on Advances in Computational Science and Engineering10.1007/978-981-97-2977-7_43(693-704)Online publication date: 3-Sep-2024
  • (2023)Sarcasm Detection of Newspaper Headlines Using LSTM-RNN2023 5th International Conference on Advances in Computing, Communication Control and Networking (ICAC3N)10.1109/ICAC3N60023.2023.10541797(604-608)Online publication date: 15-Dec-2023
  • (2023)A Transformer-based Generative Adversarial Learning to Detect Sarcasm from Bengali Text with Correct Classification of Confusing TextHeliyon10.1016/j.heliyon.2023.e22531(e22531)Online publication date: Nov-2023
  • (2022)Note: A Sociomaterial Perspective on Trace Data Collection: Strategies for Democratizing and Limiting BiasProceedings of the 5th ACM SIGCAS/SIGCHI Conference on Computing and Sustainable Societies10.1145/3530190.3534835(569-573)Online publication date: 29-Jun-2022
  • (2022)Sarcasm Detection of Textual Data on Online SocialMedia: A Review2022 2nd International Conference on Advance Computing and Innovative Technologies in Engineering (ICACITE)10.1109/ICACITE53722.2022.9823869(1981-1985)Online publication date: 28-Apr-2022
  • (2022)Sarcasm Over Time and Across Platforms: Does the Way We Express Sarcasm Change?IEEE Access10.1109/ACCESS.2022.317486210(55958-55987)Online publication date: 2022
  • (2022)Unparalleled sarcasm: a framework of parallel deep LSTMs with cross activation functions towards detection and generation of sarcastic statementsLanguage Resources and Evaluation10.1007/s10579-022-09622-357:2(765-802)Online publication date: 2-Oct-2022
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