Humor Detection in Persian: A Transformers-Based Approach

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 ۸۴.۷% on the test set. Moreover, This research introduced a dataset of ۱۴,۹۴۶ automatically-labeled tweets for humor detection in Persian.

کلیدواژه ها:

Humor Detection ، Sentiment Analysis ، Natural Language Processing ، Deep learning ، Persian language