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Sarcasm detection in news headlines using supervised learning

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dc.contributor.author Mahesh, Jayashree
dc.date.accessioned 2025-02-17T09:17:24Z
dc.date.available 2025-02-17T09:17:24Z
dc.date.issued 2024-07
dc.identifier.uri https://link.springer.com/chapter/10.1007/978-3-031-67437-2_36
dc.identifier.uri http://dspace.bits-pilani.ac.in:8080/jspui/handle/123456789/17809
dc.description.abstract The traditional work environment has seen remarkable changes as a result of the paradigm shift towards remote work, that has had a significant impact on employee well-being. This paper aims to use the Analytic Hierarchy Process (AHP), a multi-criteria decision-making (MCDM), to methodologically examine the factors affecting employee well-being in remote work contexts. By synthesizing data from 14 remote workers, this study aims to rank key factors and sub-factors that are identified with the help of content analysis. Through the AHP methodology, the relative importance of these factors is determined, which sheds light on their impact on the overall well-being of remote employees. On the basis of the results, the factors can be ranked in order- mental health support, work-life balance, physical well-being, social connection, and leadership engagement. The top five rated priority sub-factors are the promotion of mindfulness, defined boundaries, stress management, healthy eating awareness, and support for managing workload. The contribution of this research is to provide valuable insights for organizations and decision-makers to formulate strategies and initiatives to foster a supportive and conducive remote work environment, ultimately enhancing productivity and employee well-being. en_US
dc.language.iso en en_US
dc.publisher Springer en_US
dc.subject Management en_US
dc.subject Analytic hierarchy process (AHP) en_US
dc.subject Multi-criteria decision-making (MCDM) en_US
dc.title Sarcasm detection in news headlines using supervised learning en_US
dc.type Article en_US


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