Dataset on sentiment-based cryptocurrency-related news and tweets in English and Malay language.
Cryptocurrency trading is becoming popular due to its profitable investment and has led to worldwide involvement in buying and selling cryptocurrency assets. Sentiments expressed by cryptocurrency enthusiasts toward some news via social media or other online platforms may affect the cryptocurrency m...
| Publicado en: | Language Resources & Evaluation Vol. 59; no. 2; pp. 807 - 843 |
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| Autores principales: | , , |
| Formato: | Artículo |
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Springer Nature
Jun2025
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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=185240031&site=ehost-live header: @attributes: shortDbName: hlh uiTerm: 185240031 longDbName: Humanities International Complete uiTag: AN controlInfo: bkinfo: jinfo: jid: 1574020X 179V jtl: Language Resources & Evaluation issn: 1574020X maglogo: N pubinfo: dt: Jun2025 vid: 59 iid: 2 pid: 237 pub: Springer Nature artinfo: ui: 185240031 10.1007/s10579-024-09733-z ppf: 807 ppct: 36 formats: fmt: – @attributes: type: T – @attributes: type: P size: 3.3MB tig: atl: Dataset on sentiment-based cryptocurrency-related news and tweets in English and Malay language. aug: au: Mohamad Zamani, Nur Azmina Kamaruddin, Norhaslinda Yusof, Ahmad Muhyiddin B. affil: https://ror.org/05n8tts92 College of Computing, Informatics and Mathematics, Universiti Teknologi MARA, 40450, Shah Alam, Selangor, Malaysia https://ror.org/05n8tts92 College of Computing, Informatics and Mathematics, Universiti Teknologi MARA Perak Branch, Tapah Campus, 35400 Tapah Road, Perak, Malaysia https://ror.org/05n8tts92 Institute for Big Data Analytics and Artificial Intelligence (IBDAAI), Universiti Teknologi MARA (UiTM), Kompleks Al-Khawarizmi, 40450, Shah Alam, Selangor, Malaysia https://ror.org/05n8tts92 Academic of Language Studies, Pusat Asasi, Universiti Teknologi MARA Selangor Campus, 43800, Dengkil, Selangor, Malaysia su: Cryptocurrency exchanges Malay language Cryptocurrencies Deep learning Market volatility Microblogs sug: subj: Cryptocurrency exchanges Malay language Cryptocurrencies Deep learning Market volatility Microblogs keyword: Cryptocurrency Dataset Malay texts Sentiment corpora ab: Cryptocurrency trading is becoming popular due to its profitable investment and has led to worldwide involvement in buying and selling cryptocurrency assets. Sentiments expressed by cryptocurrency enthusiasts toward some news via social media or other online platforms may affect the cryptocurrency market activities. Thus, it has become a challenge to determine the level of positivity or negativity (regression) inhibiting the texts than simply classifying the sentiment into categorical classes. Regression offers more detailed information than a simple classification which can be robust to noisy data as they consider the entire range of possible target values. On the contrary, classification can lead to biased models due to imbalanced dataset and tend to cause overfitting. Hence, this work emphasises in creating sentiment-based cryptocurrency-related corpora in English and Malay focusing on Bitcoin and Ethereum. The data was collected from January to December 2021 from the publicly available news online and tweets from Twitter in English and Malay. The dataset contains a total of 29,694 instances comprised of 5694 news data and 24,000 tweets data. During the annotation process, the annotators are trained until Krippendorf's alpha agreement of above 60% is achieved since it is considered an applicable benckmark due to the annotation complexity. The corpora is available on Github for cryptocurrency-related experiments using various machine learning or deep learning models to study English and Malay sentiments effect on the global market, particularly the Malaysian market and can be extended for further analysis for Bitcoin and Ethereum market volatile nature. 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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