Work place: Department of Computer Science, Faculty of Science, University of Ibadan, Nigeria
Olufade F.W. Onifade is researcher and facilitator in the Department of Computer Science, University of Ibadan, Nigeria. He is an Associate Professor of Computer Science, the Group Lead of the Pattern Recognition, Robotics, and Intelligent Analytics Group (PRIAG) at the University of Ibadan, Ibadan, Nigeria. A recipient of French government double PhD grant leading to the award of PhD from University of Ibadan, Nigeria and Nancy 2 University, France in 2010 with specialization in Fuzzy systems, Image Analysis and Pattern Recognition, Predictive Technologies and Information Retrieval. He has published over 100 papers in both local and international refereed journals and conferences and has held several fellowships including ETT-MIT and the CV Raman Fellowship for African Researchers in India. He is a Senior Visiting Fellow to the International Centre for Theoretical Physics (ICTP), Italy.
Dr. Onifade is a member of IEEE, IAENG and CPN. He is the current Deputy Director (Academics), Distance Learning Centre, University of Ibadan, Nigeria. He loves music and travelling.
DOI: https://doi.org/10.5815/ijisa.2023.06.01, Pub. Date: 8 Dec. 2023
In computational study and automatic recognition of opinions in free texts, certain words in sentences are used to decide its sentiments. While analysing each customer’s opinion per time in churn management will be effective for personalised recommendations. Oftentimes, the opinion is not sufficient for contextualised content mining. While personalised recommendations are time consuming, it also does not provide complete picture of an overall sentiment in the business community of customers. To help businesses identify widespread issues affecting a large segment of their customers towards engendering patterns and trends of different customer churn behaviour, here, we developed a clustered contextualised conversation as opinions set for integration with Roberta Model. The developed churn behavioural opinion clusters disambiguated short messages while charactering contents collectively based on context beyond keyword-based sentiment matching for effective mining. Based on the predicted opinion threshold, customer churn category for group-based personalised decision support was generated, with matching concepts. The baseline RoBERTa model on the contextually clustered opinions, trained with a batch size of 16, a learning rate of 2e-5, over 8 epochs, using a maximum sequence length of 128 and standard hyperparameters, achieved an accuracy of 92%, Precision of 88%, Recall of 86% and F1 score of 84% over a test set of 30%.[...] Read more.
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