"Approaches to sentiment analysis of Hungarian political news at the sentence level".
Automated sentiment analysis of textual data is one of the central and most challenging tasks in political communication studies. However, the toolkits available are primarily for English texts and require contextual adaptation to produce valid results—especially concerning morphologically rich lang...
| Publicado en: | Language Resources & Evaluation Vol. 58; no. 4; pp. 1233 - 1262 |
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| Autores principales: | , , , , |
| Formato: | Artículo |
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Springer Nature
Dec2024
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| Materias: | |
| 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=180627306&site=ehost-live header: @attributes: shortDbName: hlh uiTerm: 180627306 longDbName: Humanities International Complete uiTag: AN controlInfo: bkinfo: jinfo: jid: 1574020X 179V jtl: Language Resources & Evaluation issn: 1574020X maglogo: N pubinfo: dt: Dec2024 vid: 58 iid: 4 pid: 237 pub: Springer Nature artinfo: ui: 180627306 10.1007/s10579-023-09717-5 ppf: 1233 ppct: 29 formats: fmt: – @attributes: type: T – @attributes: type: P size: 1MB tig: atl: "Approaches to sentiment analysis of Hungarian political news at the sentence level". aug: au: Ring, Orsolya Szabó, Martina Katalin Guba, Csenge Váradi, Bendegúz Üveges, István affil: https://ror.org/04vr0gs97 HUN-REN Centre for Social Sciences, Institute for Political Science, Tóth Kálmán Utca 4, 1097, Budapest, Hungary HUN-REN Centre for Social Sciences, CSS-RECENS Research Group, Budapest, Hungary https://ror.org/01pnej532 Institute of Informatics, University of Szeged, Szeged, Hungary https://ror.org/01pnej532 Doctoral School in Linguistics, University of Szeged, Szeged, Hungary https://ror.org/01jsq2704 Faculty of Social Sciences, Eötvös Loránd University, Budapest, Hungary su: Language models Machine learning Sentiment analysis Political communication Content analysis sug: subj: Language models Machine learning Sentiment analysis Political communication Content analysis keyword: BERT model Dictionary-based methods Hungarian political news Machine learning approaches Sentence-level analysis ab: Automated sentiment analysis of textual data is one of the central and most challenging tasks in political communication studies. However, the toolkits available are primarily for English texts and require contextual adaptation to produce valid results—especially concerning morphologically rich languages such as Hungarian. This study introduces (1) a new sentiment and emotion annotation framework that uses inductive approaches to identify emotions in the corpus and aggregate these emotions into positive, negative, and mixed sentiment categories, (2) a manually annotated sentiment data set with 5700 political news sentences, (3) a new Hungarian sentiment dictionary for political text analysis created via word embeddings, whose performance was compared with other available sentiment dictionaries. (4) Because of the limitations of sentiment analysis using dictionaries we have also applied various machine learning algorithms to analyze our dataset, (5) Last but not least to move towards state-of-the-art approaches, we have fine-tuned the Hungarian BERT-base model for sentiment analysis. Meanwhile, we have also tested how different pre-processing steps could affect the performance of machine-learning algorithms in the case of Hungarian texts. pubtype: Academic Journal doctype: Article src: R language: English refInfo: copyright: @attributes: flag: Y custom: Language Resources & Evaluation is a copyright of Springer, 2024. All Rights Reserved. item: Language Resources & Evaluation holder: Springer Nature dt: @attributes: year: 2024 holdings: @attributes: islocal: N |
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