Title: Context-specific multi-class data analytics for improving online conversation through deep learning

Authors: K. Dhanasekaran; T. Nadana Ravishankar; S.B. Goyal; Sardar M.N. Islam

Addresses: School of Computing, SRM Institute of Science and Technology, Irungalur, Tiruchirappalli, Tamil Nadu, India ' Department of Data Science and Business Systems, School of Computing, CET, SRM Institute of Science and Technology, Kattankulathur-603 203, Chennai, India ' Faculty of Information Technology, City University, Petaling Jaya, 46100, Malaysia ' ISILC, Victoria University, Melbourne, Australia

Abstract: Social networks have emerged as a platform for disseminating information rapidly to friends, relatives, and the public. An effective text classification strategy can improve the effectiveness of online discussion. This has been a great motivation behind text analytics research. Several text classification approaches have been developed to enhance information extraction performance and address its challenges. However, traditional text data analytics are based on limited contextual and static resources and require effective intelligent techniques for automatically extracting features from the container. To address these issues, we proposed and developed a unique context-specific multi-class data analytics architecture based on deep learning, this approach improved the performance of data analytics and mainly focused on extracting various types of information that describe several attributes to improve the online conversation. The experimental results showed that the proposed multi-class data analytics provide promising results over classification accuracy, validation accuracy, validation loss, precision, recall, and F1-measure in support of text classification for information extraction.

Keywords: convolutional neural network; data analytics; information extraction; clustering; deep learning.

DOI: 10.1504/IJDMMM.2026.154479

International Journal of Data Mining, Modelling and Management, 2026 Vol.18 No.2, pp.120 - 136

Received: 06 Mar 2024
Accepted: 14 Jun 2024
Published online: 30 Jun 2026 *

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