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 Jiby Jose, E.; Biswas, Priyanka Title A name given to the resource Next Gen Text Mining in English Literature: A Machine Learning Approach for Narrative and Stylistic Analysis 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 2025 IEEE 5th International Conference on ICT in Business Industry and Government, ICTBIG 2025; Identifier An unambiguous reference to the resource within a given context <a href="https://doi.org/10.1109/ICTBIG68706.2025.11323969" target="_blank" rel="noreferrer noopener">https://doi.org/10.1109/ICTBIG68706.2025.11323969</a> <br /><br /><a href="https://www.scopus.com/pages/publications/105032841648?origin=resultslist" target="_blank" rel="noreferrer noopener">https://www.scopus.com/pages/publications/105032841648?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 Jiby Jose E., CHRIST (Deemed to Be University), Bangalore, India; Biswas P., CHRIST (Deemed to Be University), Bangalore, India Description An account of the resource This paper presents a research work that is novel in nature, based on a new machine learning framework that focuses on a computational analysis in the field of English literature. This research uniquely aims at focusing on an intersection based on stylistic patterns and narrative structures. The newly proposed model is termed the 'Next Gen Text Mining Framework', which leverages transformer-based models (such as GPT, BERT), along with sentiment trajectory-based modeling, network analysis, and clustering algorithms for extracting stylistic features and latent semantics from the text on a large scale. A meticulously used re-processing type pipelining framework modifies and prepares the data for the model ingestion. The multi-modal approach used in this framework enables the experimental analysis of different models across various diverse corpora in the literature that demonstrate stability, superior accuracy, and robustness when compared to the traditional models, in tasks like the authorship attribution, sentimental trajectory mapping, and the theme-based classification. The proposed framework bridges the gap between distant and close reading practices, enhancing the pedagogically based engagements and translating the computational insights into interpretative forms for research and teaching. This research highlights a scalable and replicable framework model that is a transformative tool when a large-scale inquiry in literature is considered and sets a foundation towards multi-modal, future, cross-lingual, and multidisciplinary-based applications. 2025 IEEE. Subject The topic of the resource Computational Literary Studies; Machine Learning; Narrative Analysis; Stylistic Analysis; Text Mining; Transformer Models Publisher An entity responsible for making the resource available Institute of Electrical and Electronics Engineers Inc. Relation A related resource ISBN: 979-833157981-4; 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