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 Article Faculty Publications -Articles 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 Yadav, Priyanka; Yadav, Manoj; Bokan, Reena; Poonia, Ramesh Chandra Title A name given to the resource Sentiment analysis of sentences used in Indian local languages using Transfer Learning Date A point or period of time associated with an event in the lifecycle of the resource 01-01-2026 Source A related resource from which the described resource is derived Advances in Computers;Volume;143;pp.463-481 Identifier An unambiguous reference to the resource within a given context <a href="https://doi.org/10.1016/bs.adcom.2026.01.012" target="_blank" rel="noreferrer noopener">https://doi.org/10.1016/bs.adcom.2026.01.012</a> <br /><br /><a href="https://www.scopus.com/pages/publications/105030083934?origin=resultslist" target="_blank" rel="noreferrer noopener">https://www.scopus.com/pages/publications/105030083934?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 Yadav P., Guru Jambheshwar University of Science &amp; Technology, Haryana, Hisar, India; Yadav M., Guru Jambheshwar University of Science &amp; Technology, Haryana, Hisar, India; Bokan R., Gurugram University, Haryana, Gurugram, India; Poonia R.C., Department of Computer Science, CHRIST University, Bangalore, India Description An account of the resource The growing number of online communication channels has led to a dramatic increase in the volume of user-generated content across all media. Sentiment analysis is given more force by having access to these varying perspectives and feelings. Due to a lack of standardised labelled data, sentiment analysis becomes much more difficult. The goal of sentiment analysis is to determine, from a text sample, an individual's likely emotional reaction to a given event or point of view. When estimating the tone of a text, polarity analysis is commonly used. Negative, Positive and neutral are the labels used by sentiment classifiers. This chapter provides a framework for effective sentiment analysis of sentences from four local language datasets, including Hindi, English, Bangla, and Marathi, using transfer learning. The results show that the BERT model achieves a high level of accuracy when evaluated using random forests, nae Bayes, and decision trees. 2026 Subject The topic of the resource BERT; GitHub; Sentiment analysis; Transfer learning methods Publisher An entity responsible for making the resource available Academic Press Inc. Relation A related resource ISSN: 652458; ISBN: 978-044331710-1; Language A language of the resource English Type The nature or genre of the resource Article 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