Humor Detection in Persian: A Transformers-Based Approach

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Najafi Lapavandani Fateme; Shirali Shahreza Mohammad Hasan

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Journal Paper

Paper Information

Journal: International Journal of Information and Communication Technology Research Year:2023 | Volume:15 | Issue:1 Page(s): 56-62

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Information Journal Paper

Title

Humor Detection in Persian: A Transformers-Based Approach

Author(s)

Najafi Lapavandani Fateme | Shirali Shahreza Mohammad Hasan | Issue Writer Certificate

Keywords

Humor Detection

Sentiment Analysis

Natural Language Processing

Deep learning

Persian language

Abstract

Humor is a linguistic device that can make people laugh, and in the case of expressing opinions, it can transform a phrase's polarity. Humorous sentences presenting ideas and criticism, occasionally using informal forms, have made their way to social media platforms like Twitter in almost every domain. Persian speakers likewise express their opinions through humorous tweets on Twitter. As one of the early efforts for detecting humor in Persian, the current research proposes a model by fine-tuning a transformer-based language model on a Persian Humor Detection dataset. The proposed model has an accuracy of 84. 7% on the test set. Moreover, This research introduced a dataset of 14, 946 automatically-labeled tweets for Humor Detection in Persian.

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