Annotated dataset for sentiment analysis and sarcasm detection: Bilingual code-mixed English-Malay social media data in the public security domain.

Autor:	Md Suhaimin MS; Data Technology and Applications Research Group, Faculty of Computing and Informatics, Universiti Malaysia Sabah, Kota Kinabalu 88400, Sabah, Malaysia.; Polytechnic and Community College Education Department, Galeria PjH Aras 4-7, Jalan P4W Persiaran Perdana, 62100 Putrajaya, Malaysia., Ahmad Hijazi MH; Data Technology and Applications Research Group, Faculty of Computing and Informatics, Universiti Malaysia Sabah, Kota Kinabalu 88400, Sabah, Malaysia.; Creative Advanced Machine Intelligence Research Centre, Faculty of Computing and Informatics, Universiti Malaysia Sabah, Kota Kinabalu 88400, Sabah, Malaysia., Moung EG; Data Technology and Applications Research Group, Faculty of Computing and Informatics, Universiti Malaysia Sabah, Kota Kinabalu 88400, Sabah, Malaysia.
Jazyk:	angličtina
Zdroj:	Data in brief [Data Brief] 2024 Jun 24; Vol. 55, pp. 110663. Date of Electronic Publication: 2024 Jun 24 (Print Publication: 2024).
DOI:	10.1016/j.dib.2024.110663
Abstrakt:	Sentiment analysis in the public security domain involves analysing public sentiment, emotions, opinions, and attitudes toward events, phenomena, and crises. However, the complexity of sarcasm, which tends to alter the intended meaning, combined with the use of bilingual code-mixed content, hampers sentiment analysis systems. Currently, limited datasets are available that focus on these issues. This paper introduces a comprehensive dataset constructed through a systematic data acquisition and annotation process. The acquisition process includes collecting data from social media platforms, starting with keyword searching, querying, and scraping, resulting in an acquired dataset. The subsequent annotation process involves refining and labelling, starting with data merging, selection, and annotation, ending in an annotated dataset. Three expert annotators from different fields were appointed for the labelling tasks, which produced determinations of sentiment and sarcasm in the content. Additionally, an annotator specialized in literature was appointed for language identification of each content. This dataset represents a valuable contribution to the field of natural language processing and machine learning, especially within the public security domain and for multilingual countries in Southeast Asia. (© 2024 The Authors.)
Databáze:	MEDLINE
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