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Ontology-Based Natural Language Processing for Sentimental Knowledge Analysis Using Deep Learning Architectures

Published: 15 January 2024 Publication History

Abstract

When tested with popular datasets, sentiment categorization using deep learning (DL) algorithms will produce positive results. Building a corpus on novel themes to train machine learning methods in sentiment classification with high assurance, however, will be difficult. This study proposes a way for representing efficient features of a dataset into a word embedding layer of DL methods in sentiment classification known as KPRO (knowledge processing and representation based on ontology), a procedure to embed knowledge in the ontology of opinion datasets. This research proposes novel methods in ontology-based natural language processing utilizing feature extraction as well as classification by a DL technique. Here, input text has been taken as web ontology based text and is processed for word embedding. Then the feature mapping is carried out for this processed text using least square mapping in which the sentiment-based text has been mapped for feature extraction. The feature extraction is carried out using a Markov model based auto-feature encoder (MarMod_AuFeaEnCod). Extracted features are classified by utilizing hierarchical convolutional attention networks. Based on this classified output, the sentiment of the text obtained from web data has been analyzed. Results are carried out for Twitter and Facebook ontology-based sentimental analysis datasets in terms of accuracy, precision, recall, F-1 score, RMSE, and loss curve analysis. For the Twitter dataset, the proposed MarMod_AuFeaEnCod_HCAN attains an accuracy of 98%, precision of 95%, recall of 93%, F-1 score of 91%, RMSE of 88%, and loss curve of 70.2%. For Facebook, ontology web dataset analysis is also carried out with the same parameters in which the proposed MarMod_AuFeaEnCod_HCAN acquires accuracy of 96%, precision of 92%, recall of 94%, F-1 score of 91%, RMSE of 77%, and loss curve of 68.2%.

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  • (2024)An NLP Approach to Enrich Biomedical Research Through Sentiment Analysis of Patient FeedbackRevolutionizing AI with Brain-Inspired Technology10.4018/979-8-3693-6303-4.ch007(155-188)Online publication date: 4-Oct-2024
  • (2024)Matching heterogeneous ontologies via transfer learningAlexandria Engineering Journal10.1016/j.aej.2024.08.010105(449-459)Online publication date: Oct-2024

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  1. Ontology-Based Natural Language Processing for Sentimental Knowledge Analysis Using Deep Learning Architectures

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        cover image ACM Transactions on Asian and Low-Resource Language Information Processing
        ACM Transactions on Asian and Low-Resource Language Information Processing  Volume 23, Issue 1
        January 2024
        385 pages
        EISSN:2375-4702
        DOI:10.1145/3613498
        Issue’s Table of Contents

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        Association for Computing Machinery

        New York, NY, United States

        Publication History

        Published: 15 January 2024
        Online AM: 14 November 2023
        Accepted: 30 March 2023
        Revised: 07 March 2023
        Received: 02 June 2022
        Published in TALLIP Volume 23, Issue 1

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        Author Tags

        1. Ontology
        2. NLP
        3. KPRO
        4. deep learning
        5. feature extraction
        6. classification

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        • Deanship of Scientific Research at King Khalid University, Abha, Kingdom of Saudi Arabia

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        • (2024)An NLP Approach to Enrich Biomedical Research Through Sentiment Analysis of Patient FeedbackRevolutionizing AI with Brain-Inspired Technology10.4018/979-8-3693-6303-4.ch007(155-188)Online publication date: 4-Oct-2024
        • (2024)Matching heterogeneous ontologies via transfer learningAlexandria Engineering Journal10.1016/j.aej.2024.08.010105(449-459)Online publication date: Oct-2024

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