000			03983ntm a2200337 i 4500
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005			20260805094658.0
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008			260805t20262026my a|||fr|||| 000 0 eng d
020			_aTHE0010798 (Local) _qHardback
040			_aUMPSA _beng _cUMPSA _erda
090			_aFK .B46 2026 r Thesis
100	1		_aBhowmik Abhijit, _eauthor.
245	1	0	_aEDUAE-LSTM : _cBhowmik Abhijit _ba model for detecting fake reviews in the education using an educational attention-enhanced lstm and random forest technique /
264		1	_aKuantan, Pahang : _bUMPSA, _c2025
264		4	_c© 2025
300			_axiii, 129 pages : _billustrations ; _c30 cm. + _e1 CD-ROM.
336			_2rdacontent _atext
337			_2rdamedia _aunmediated
338			_2rdacarrier _avolume
347			_2rda _atext file _bPDF
500			_aFaculty of Computing
502			_aThesis (Doctor of Philosophy) -- Universiti Malaysia Pahang - 2026
504			_aIncludes bibliographical references
520	3		_aThe increasing reliance on online student review systems in higher education has intensified the need to ensure the authenticity and reliability of such feedback. Educational reviews differ significantly from product and service reviews due to multi-aspect sentiments, domain-specific terminology, limited metadata availability, and strict privacy regulations. These unique characteristics make existing fake review detection methods from other domains unsuitable for academic settings, where rating–comment inconsistencies, subjective bias, and strategically manipulated evaluations pose challenges to fair and data-driven teacher assessment. This research aims to address these challenges by developing a hybrid analytical framework specifically designed for the educational domain by fulfilling three objectives namely, i) Feature identification, ii) Model development and iii) Evaluation of the developed model. The proposed approach integrates an Education Attention-Enhanced Long Short-Term Memory model (EduAE-LSTM) for aspect-based sentiment extraction with a Random Forest classifier for fake review detection. A large-scale teacher evaluation dataset from the American International University-Bangladesh (AIUB), containing over 2.2 million entries, was utilized alongside benchmark datasets such as SemEval-2014 Task 4, MOOC Reviews, and Coursera Course Reviews. Linguistic, contextual, and behavioral features, including polarity, subjectivity, rating–comment consistency, and review length were operationalized to enable accurate and interpretable classification. Experimental results demonstrate that the EduAE-LSTM surpasses standard CNN, GRU, and LSTM baselines, achieving an F1-score of 0.86 and ROC-AUC of 0.95. When integrated with the Random Forest classifier, the hybrid model achieved 96.9% accuracy, 0.9999 precision, 0.9620 recall, and a 0.9806 F1-score, outperforming all comparative machine learning models. Domain validation using expert judgments yielded a 96% agreement rate and no significant difference in predictions (p > 0.05). Cross-domain evaluations further confirmed the robustness of the framework, with F1-scores ranging from 0.94 to 0.99 across benchmark datasets. This research contributes both theoretically and practically by advancing NLP methodologies for education-specific text mining, proposing a validated hybrid deep+ensemble architecture for fake review detection, and offering an interpretable, data-driven tool to strengthen transparency and trust in academic evaluation systems. The societal impact aligns with SDG 4 (Quality Education) by enhancing the credibility of educational assessments, and SDG 16 (Peace, Justice, and Strong Institutions) through promoting ethical, trustworthy, and data-driven institutional decision-making.
610	2	0	_aFaculty of Computing _xDissertations
650		0	_aUniversities and colleges _xDissertations
650		0	_aTheses _xDissertations
942			_2lcc _cTHESIS
999			_c105393 _d105399