FACULTY OF SCIENCE
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Item Improvement on emotional variance analysis technique (EVA) for sentiment analysis in healthcare service delivery(Foundation of Computer Science FCS, New York, USA, 2024-05) Agada, V. E.; Ojo, A. K.This research introduces an innovative approach to improving sentiment analysis in healthcare service delivery by integrating Emotion and Affect Recognition (EAR) techniques into Emotional Variance Analysis (EVA). Leveraging logistic regression, the modifications, including adjusting confidence thresholds and utilizing the Rectified Linear Unit (ReLU) function, aim to address high polarity and enable real-time analysis. The methodology outlines a systematic process for EAR integration, offering practical insights for healthcare practitioners. In this study, additional datasets, including the Healthcare Patient Satisfaction Data Collection, the 9 Popular Patient Portal App Reviews for November 2023, and the HCAHPS Hospital Ratings Survey, are incorporated to enhance the robustness and reliability of the approach. The results across three healthcare centers demonstrate the effectiveness of this augmented approach, with comparisons against existing models using performance metrics. While showcasing promising potential, further research is needed to explore scalability and generalizability.Item Trend analysis in academic journals in computer science using text mining(IJCSIS Publication, 2015-04) Ojo, A. K.; Adeyemo, A. B.Text mining is the process of discovering new, hidden information from texts- structured, semi-structured and unstructured. There are so many benefits, valuable insights, discoveries and useful information that can be derived from unstructured or semi- unstructured data. In this study, text mining techniques were used to identify trends of different topics that exist in the text and how they change over time. Keywords were crawled from the abstracts in Journal of Computer Science and Technology (JCST), one of the ISI indexed journals in the field of Computer Science from 1993 to 2013. Results of our analysis clearly showed a varying trend in the representation of various subfields in a Computer Science journal from decade to decade. It was discovered that the research direction was changing from pure mathematical foundations, Theory of Computation to Applied Computing, Artificial Intelligence in form of Robotics and Embedded Systems.Item Improving information acquisition via text mining for efficient e-governance(2015-03) Adeyemo, A. B.; Ojo, A. K.In this paper we proposed a framework for integrating text mining with E-Governance. We suggested that the users of electronic governance can use the text terms to describe their interest which can be processed for clustering and term extraction. The words thus expressed by users are tracked and subjected to processing wherein it is possible to generate content. We have provided the framework and tested it in a few web sites. We have used the clustering and pre-processing for the content management. The results are encouraging and it is possible to extent such exercises for other text minging processes.Item Knowledge discovery in academic electronic resources using text mining(2013-02) Ojo, A. K.; Adeyemo, A. B.Academic resources documents contain important knowledge and research results. They have highly quality information. However, they are lengthy and have much noisy results such that it takes a lot of human efforts to analyse. Text mining could be used to analyse these textual documents and extract useful information from large amount of documents quickly and automatically. In this paper, abstracts of electronic publications from African Journal of Computing and ICTs, an IEEE Nigerian Computer Chapter Publication were analysed using text mining techniques. A text mining model was developed and was used to analyse the abstracts collected. The texts were transformed into structured data in frequency form, cleaned up and the documents split into series of word features (adjectives, verbs, adverbs, nouns) and the necessary words were extracted from the documents. The corpus collected had 1637 words. The word features were then analysed by classifying and clustering them. The text mining model developed is capable of mining texts from academic electronic resources thereby identifying the weak and strong issues in those publications.