Improving irony speech spreaders profiling on social networks using clustering & transformer based models. - Hazrati, Leila; Sokhandan, Alireza; Farzinvash, Leili (Language Resources & Evaluation, 2025)
As hidden information discovery is fundamental in AI research, natural language researchers have become interested in extracting meaningful information from social networks. One source of such hidden information is irony in text, which has grown significantly on these platforms. Since social media allows anyone to publish any content, understanding irony is crucial for grasping the true meaning of a text. Detecting irony and identifying authors of ironic texts can be instrumental in analyzing customer reviews, gauging public sentiment about brands or events, and uncovering implicit feedback. This research proposes a novel method that utilizes clustering of user writings with importance-based weighting, along with transformer-based models, to detect authors with a penchant for ironic expression. Recognizing the conversational nature of social media text, the method incorporates pre-processing techniques to enhance detection accuracy. Evaluation on a Twitter dataset demonstrates the efficacy of the proposed method, achieving a 98% accuracy rate in classifying authors as ironic or non-ironic.