The proposed Katydid-Inspired BERT Optimization for Improved Sentence Semantics in Text Classification (KyBERT), aims to optimize the ability of the model to identify more complex linguistic patterns along with long-range dependencies. Bio-inspired optimization on KyBERT, where the inspiration is drawn from the katydid's sensory capabilities, enhances better handling with context-rich and resource-dense language. Generally these models enhance the values of key metrics like classification accuracy, F-measure, and Fowlkes-Mallows Index (FMI) since sentence semantics was poorly represented in simple models. This proposed model KyBERT has also confirmed that useful in creating a much-refined framework, that rarely gives false predictions and significantly improves text classification rate. The evaluation based on the Amazon Reviews dataset with 476,001 unique IDs established a high performance of text classification with 64.29%, F-measure of 64.67% and FMI of 64.68%.
Keywords
Sentence Semantics, KyBERT, Katydid Optimization, BERT, Text Classification, Sentiment Analysis, Bio Inspired Models.
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CRediT Author Statement
The authors confirm contribution to the paper as follows:
Conceptualization: Prativa Mishra, Gopalakrishnan T and Parameswaran T;
Methodology: Prativa Mishra;
Software: Gopalakrishnan T and Parameswaran T;
Data Curation: Prativa Mishra;
Writing-Original Draft Preparation: Prativa Mishra, Gopalakrishnan T and Parameswaran T;
Visualization: Gopalakrishnan T and Parameswaran T;
Investigation: Prativa Mishra;
Supervision: Gopalakrishnan T and Parameswaran T;
Validation: Parameswaran T;
Writing- Reviewing and Editing: Prativa Mishra, Gopalakrishnan T and Parameswaran T;
All authors reviewed the results and approved the final version of the manuscript.
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Gopalakrishnan T
Department of Information Technology, Manipal Institute of Technology Bengaluru, Manipal Academy of Higher Education, Manipal, India.
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Cite this article
Prativa Mishra, Gopalakrishnan T and Parameswaran T, “Katydid Inspired BERT Optimization for Improved Sentence Semantics in Text Classification”, Journal of Machine and Computing, vol.6, no.1, pp. 114-130, 2026, doi: 10.53759/7669/jmc202606009.
