Improving irony speech spreaders profiling on social networks using clustering & transformer based models.
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 a...
| Publicado en: | Language Resources & Evaluation Vol. 59; no. 3; pp. 2297 - 2328 |
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| Autores principales: | , , |
| Formato: | Artículo |
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Springer Nature
Sep2025
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| Acceso en línea: | Ver este registro en EBSCOhost |
| fields | @attributes: recordID: 1 pdfLink: plink: https://search.ebscohost.com/login.aspx?direct=true&db=hlh&AN=186909074&site=ehost-live header: @attributes: shortDbName: hlh uiTerm: 186909074 longDbName: Humanities International Complete uiTag: AN controlInfo: bkinfo: jinfo: jid: 1574020X 179V jtl: Language Resources & Evaluation issn: 1574020X maglogo: N pubinfo: dt: Sep2025 vid: 59 iid: 3 pid: 237 pub: Springer Nature artinfo: ui: 186909074 10.1007/s10579-025-09815-6 ppf: 2297 ppct: 31 formats: fmt: – @attributes: type: T – @attributes: type: P size: 1.3MB tig: atl: Improving irony speech spreaders profiling on social networks using clustering & transformer based models. aug: au: Hazrati, Leila Sokhandan, Alireza Farzinvash, Leili affil: https://ror.org/01papkj44 Computer Eng. Department, Faculty of Electrical and Computer Engineering, University of Tabriz, Tabriz, Iran su: Social networks Transformer models Classification Sentiment analysis Natural language processing Clustering algorithms sug: subj: Social networks Transformer models Classification Sentiment analysis Natural language processing Clustering algorithms keyword: Author Profiling Clustering Information and Computing Sciences Artificial Intelligence and Image Processing Information Systems Irony detection Preprocessing Transformers ab: 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. pubtype: Academic Journal doctype: Article src: R language: English refInfo: copyright: @attributes: flag: Y custom: Language Resources & Evaluation is a copyright of Springer, 2025. All Rights Reserved. item: Language Resources & Evaluation holder: Springer Nature dt: @attributes: year: 2025 holdings: @attributes: islocal: N |
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