CIC@PAN: Simplifying Irony Profiling using Twitter Data

Sabur Butt; Fazlourrahman Balouchzahi; Grigori Sidorov; Alexander Gelbukh

CIC@PAN: Simplifying Irony Profiling using Twitter Data

Sabur Butt, Fazlourrahman Balouchzahi, Grigori Sidorov, Alexander Gelbukh

Centro de Investigación en Computación (CIC)

Research output: Contribution to journal › Conference article › peer-review

1 Scopus citations

Abstract

The article explains the model submission by the team CIC for "Profiling Irony and Stereotype Spreaders on Twitter (IROSTEREO)" at PAN 2022. Irony profiling can help in identifying stereotype spreaders and can enhance the understanding of author behaviours. We proposed a methodology focusing on feature engineering to classify irony for long texts based on multiple linguistic and emotion-based features. We also extensively discussed the shortcomings of the data and the proposed task to provide the future research direction. The paper reveals the impact of robust feature engineering with a machine learning approach on the long social media texts in the author profiles. Our method achieved an accuracy of 87.22% on the test set.

Original language	English
Pages (from-to)	2402-2410
Number of pages	9
Journal	CEUR Workshop Proceedings
Volume	3180
State	Published - 2022
Event	2022 Conference and Labs of the Evaluation Forum, CLEF 2022 - Bologna, Italy Duration: 5 Sep 2022 → 8 Sep 2022

Keywords

Feature Engineering
Figurative Language Processing
Irony profiling
Machine Learning
Stereotype spreaders

Cite this

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title = "CIC@PAN: Simplifying Irony Profiling using Twitter Data",

abstract = "The article explains the model submission by the team CIC for {"}Profiling Irony and Stereotype Spreaders on Twitter (IROSTEREO){"} at PAN 2022. Irony profiling can help in identifying stereotype spreaders and can enhance the understanding of author behaviours. We proposed a methodology focusing on feature engineering to classify irony for long texts based on multiple linguistic and emotion-based features. We also extensively discussed the shortcomings of the data and the proposed task to provide the future research direction. The paper reveals the impact of robust feature engineering with a machine learning approach on the long social media texts in the author profiles. Our method achieved an accuracy of 87.22% on the test set.",

keywords = "Feature Engineering, Figurative Language Processing, Irony profiling, Machine Learning, Stereotype spreaders",

author = "Sabur Butt and Fazlourrahman Balouchzahi and Grigori Sidorov and Alexander Gelbukh",

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AU - Butt, Sabur

AU - Balouchzahi, Fazlourrahman

AU - Sidorov, Grigori

AU - Gelbukh, Alexander

PY - 2022

Y1 - 2022

N2 - The article explains the model submission by the team CIC for "Profiling Irony and Stereotype Spreaders on Twitter (IROSTEREO)" at PAN 2022. Irony profiling can help in identifying stereotype spreaders and can enhance the understanding of author behaviours. We proposed a methodology focusing on feature engineering to classify irony for long texts based on multiple linguistic and emotion-based features. We also extensively discussed the shortcomings of the data and the proposed task to provide the future research direction. The paper reveals the impact of robust feature engineering with a machine learning approach on the long social media texts in the author profiles. Our method achieved an accuracy of 87.22% on the test set.

AB - The article explains the model submission by the team CIC for "Profiling Irony and Stereotype Spreaders on Twitter (IROSTEREO)" at PAN 2022. Irony profiling can help in identifying stereotype spreaders and can enhance the understanding of author behaviours. We proposed a methodology focusing on feature engineering to classify irony for long texts based on multiple linguistic and emotion-based features. We also extensively discussed the shortcomings of the data and the proposed task to provide the future research direction. The paper reveals the impact of robust feature engineering with a machine learning approach on the long social media texts in the author profiles. Our method achieved an accuracy of 87.22% on the test set.

KW - Feature Engineering

KW - Figurative Language Processing

KW - Irony profiling

KW - Machine Learning

KW - Stereotype spreaders

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JO - CEUR Workshop Proceedings

JF - CEUR Workshop Proceedings

Y2 - 5 September 2022 through 8 September 2022

ER -

CIC@PAN: Simplifying Irony Profiling using Twitter Data

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Keywords

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