Dublin Core The Dublin Core metadata element set is common to all Omeka records, including items, files, and collections. For more information see, http://dublincore.org/documents/dces/. Title A name given to the resource Faculty Publications Conference Paper Faculty Publications- Conference Papers Dublin Core The Dublin Core metadata element set is common to all Omeka records, including items, files, and collections. For more information see, http://dublincore.org/documents/dces/. Creator An entity primarily responsible for making the resource Dabral, Anushka; Sivakumar, R. Title A name given to the resource Machine Learning for Mental Health: A Sentiment Analysis Approach for Detecting Depressive Tendencies on LinkedIn During Layoffs Using RoBERTa Date A point or period of time associated with an event in the lifecycle of the resource 01-01-2025 Source A related resource from which the described resource is derived Smart Innovation, Systems and Technologies;Volume;413 SIST;pp.279-290 Identifier An unambiguous reference to the resource within a given context <a href="https://doi.org/10.1007/978-981-97-7717-4_19" target="_blank" rel="noreferrer noopener">https://doi.org/10.1007/978-981-97-7717-4_19</a> <br /><br /><a href="https://www.scopus.com/pages/publications/86000442424?origin=resultslist" target="_blank" rel="noreferrer noopener">https://www.scopus.com/pages/publications/86000442424?origin=resultslist</a> Coverage The spatial or temporal topic of the resource, the spatial applicability of the resource, or the jurisdiction under which the resource is relevant Dabral A., Department of Statistics and Data Science, CHRIST (Deemed to Be University), Bengaluru, India; Sivakumar R., Department of Statistics and Data Science, CHRIST (Deemed to Be University), Bengaluru, India Description An account of the resource In the present corporate set-up, layoffs are an unfortunate yet common occurrence. Such occurrences lead to loss of job security and can have direconsequences on an individual's mental health, leading to depression. Depression was a global health concern well before the current downsizing came into the picture. These trying times have acted as a catalyst for this illness that affects not just mental health but all aspects of an individuals life. The study investigates the use of sentiment analysis on LinkedIn data to identify and examine depressive tendencies among victims of layoffs. Web-scraped information was taken from LinkedIn profiles of individuals affected directly or indirectly by layoffs. RoBERTa, a transformers model, is used to classify people as depressed or not by evaluating sentiment and emotional cues. A comparison between four machine learning algorithms- Decision Tree, Logistic Regression, SVM, and Nae Bayes is drawn to check their ability to detect depression. The SVM classifier performed best with an accuracy of 95.59% and 83.52% with the CountVectorizer and TF-IDF feature selection methods, respectively. Sentiment analysis aids in this research by examining the melancholic undertones in the words and phrases used in texts authored by people affected by layoffs directly or indirectly. The knowledge gained from this research can significantly affect corporate initiatives, mental health services, and human resource practices during such challenging times. The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025. Subject The topic of the resource Depression; Layoffs; LinkedIn; Machine Learning; RoBERTa; Sentiment Analysis; TF-IDF Publisher An entity responsible for making the resource available Springer Science and Business Media Deutschland GmbH Relation A related resource ISSN: 21903018; ISBN: 978-981977716-7; Language A language of the resource English Type The nature or genre of the resource Conference paper Rights Information about rights held in and over the resource Restricted Access; Hardcopy may be available in the library Format The file format, physical medium, or dimensions of the resource online