FinnSentiment: a Finnish social media corpus for sentiment polarity annotation.
Sentiment analysis and opinion mining are essential tasks with many prominent application areas, e.g., when researching popular opinions on products or brands. Sentiments expressed in social media can be used in brand name monitoring and indicating fake news. In our survey of previous work, we note...
| Publicado en: | Language Resources & Evaluation Vol. 57; no. 2; pp. 581 - 610 |
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| Autores principales: | , , |
| Formato: | Artículo |
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Springer Nature
Jun2023
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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=163826595&site=ehost-live header: @attributes: shortDbName: hlh uiTerm: 163826595 longDbName: Humanities International Complete uiTag: AN controlInfo: bkinfo: jinfo: jid: 1574020X 179V jtl: Language Resources & Evaluation issn: 1574020X maglogo: N pubinfo: dt: Jun2023 vid: 57 iid: 2 pid: 237 pub: Springer Nature artinfo: ui: 163826595 10.1007/s10579-023-09644-5 ppf: 581 ppct: 29 formats: fmt: – @attributes: type: T – @attributes: type: P size: 955KB tig: atl: FinnSentiment: a Finnish social media corpus for sentiment polarity annotation. aug: au: Lindén, Krister Jauhiainen, Tommi Hardwick, Sam affil: University of Helsinki, Helsinki, Finland su: Social media Sentiment analysis Public opinion Annotations Fake news sug: subj: Social media Sentiment analysis Public opinion Annotations Fake news keyword: Data set Finnish Polarity Sentiment ab: Sentiment analysis and opinion mining are essential tasks with many prominent application areas, e.g., when researching popular opinions on products or brands. Sentiments expressed in social media can be used in brand name monitoring and indicating fake news. In our survey of previous work, we note that there is no large-scale social media data set with sentiment polarity annotations for Finnish. This publication aims to remedy this shortcoming by introducing a 27,000-sentence data set annotated independently with sentiment polarity by three native annotators. We had three annotators annotate the whole data set, which provides a unique opportunity for further studies of annotator behavior over the sample annotation order. We analyze their inter-annotator agreement and provide two baselines to validate the usefulness of the data set. pubtype: Academic Journal doctype: Article src: R language: English refInfo: copyright: @attributes: flag: Y custom: Language Resources & Evaluation is a copyright of Springer, 2023. All Rights Reserved. item: Language Resources & Evaluation holder: Springer Nature dt: @attributes: year: 2023 holdings: @attributes: islocal: N |
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